local dependence functions for some families of bivariate distributions and total positivity the purpose of this paper is to investigate a very useful application of a certain local dependence function c f x y which was considered recently by holland and wang 20 an interesting property of c f x y is that the underlying joint density f x y is tp 2 that is totally positive of order 2 if and only if c f x y 0 this gives an elegant way to investigate the tp 2 property of any bivariate distribution for the saramanov family the ali mikhail haq family of bivariate distributions and the family of bivariate elliptical distributions we derive the local dependence function and obtain conditions for f x y to be tp 2 these families are quite rich and include many other large classes of bivariate distributions as their special cases similar conditions are obtained for bivariate distributions with exponential conditionals and bivariate distributions with pareto conditionals

estimating variance parameters from multivariate normal variables subject to limits of detection mle reml or bayesian approaches likelihood based approaches which naturally incorporate left censoring due to limit of detection are commonly utilized to analyze censored multivariate normal data however the maximum likelihood estimator mle typically underestimates variance parameters the restricted maximum likelihood estimator reml which corrects the underestimation of variance parameters cannot be easily extended to analyze censored multivariate normal data in the light of the connection between the reml and a bayesian approach discovered in 1974 by dr harville this paper describes a bayesian approach to censored multivariate normal data this bayesian approach is justified through its link to the reml via laplace s approximation and its performance is evaluated through a simulation study we consider the bayesian approach as a valuable alternative because it yields less biased variance parameter estimates than the mle and because a solid reml is technically difficult when data are left censored copyright 2009 john wiley sons ltd

posterior likelihood methods for multivariate survival data this article deals with the semiparametric analysis of multivariate survival data with random block group effects survival times within the same group are correlated as a consequence of a frailty random block effect the standard approaches assume either a parametric or a completely unknown baseline hazard function this paper considers an intermediate solution that is a nonparametric function that is reasonably smooth this is accomplished by a bayesian model in which the conditional proportional hazards model is used with a correlated prior process for the baseline hazard the posterior likelihood based on data as well as the prior process is similar to the discretized penalized likelihood for the frailty model the methodology is exemplified with the recurrent kidney infections data of mcgilchrist and aisbett 1991 biometrics 47 461 466 in which the times to infections within the same patients are expected to be correlated the reanalysis of the data has shown that the estimates of the parameters of interest and the associated standard errors depend on the prior knowledge about the smoothness of the baseline hazard

regression survival analysis with an assumed copula for dependent censoring a sensitivity analysis approach summary in clinical studies when censoring is caused by competing risks or patient withdrawal there is always a concern about the validity of treatment effect estimates that are obtained under the assumption of independent censoring because dependent censoring is nonidentifiable without additional information the best we can do is a sensitivity analysis to assess the changes of parameter estimates under different assumptions about the association between failure and censoring this analysis is especially useful when knowledge about such association is available through literature review or expert opinions in a regres sion analysis setting the consequences of falsely assuming independent censoring on parameter estimates are not clear neither the direction nor the magnitude of the potential bias can be easily predicted we provide an approach to do sensitivity analysis for the widely used cox proportional hazards models the joint distribution of the failure and censoring times is assumed to be a function of their marginal dis tributions this function is called a copula under this assumption we propose an iteration algorithm to estimate the regression parameters and marginal survival functions simulation studies show that this algorithm works well we apply the proposed sensitivity analysis approach to the data from an aids clinical trial in which 27 of the patients withdrew due to toxicity or at the request of the patient or investigator

the use of gaussian quadrature for estimation in frailty proportional hazards models in this paper we propose a novel gaussian quadrature estimation method in various frailty proportional hazards models we approximate the unspecified baseline hazard by a piecewise constant one resulting in a parametric model that can be fitted conveniently by gaussian quadrature tools in standard software such as sas proc nlmixed we first apply our method to simple frailty models for correlated survival data e g recurrent or clustered failure times then to joint frailty models for correlated failure times with informative dropout or a dependent terminal event such as death simulation studies show that our method compares favorably with the well received penalized partial likelihood method and the monte carlo em mcem method for both normal and gamma frailty models we apply our method to three real data examples 1 the time to blindness of both eyes in a diabetic retinopathy study 2 the joint analysis of recurrent opportunistic diseases in the presence of death for hiv infected patients and 3 the joint modeling of local distant tumor recurrences and patients survival in a soft tissue sarcoma study the proposed method greatly simplifies the implementation of the joint frailty models and makes them much more accessible to general statistical practitioners

bivariate extreme value theory models and estimation summary bivariate extreme value distributions arise as the limiting distributions of renormalized componentwise maxima no natural parametric family exists for the dependence between the marginal distributions but there are considerable restrictions on the dependence structure we consider modelling the dependence function with parametric models for which two new models are presented tests for independence and discriminating between models are also given the estimation procedure and the flexibility of the new models are illustrated with an application to sea level data extreme value theory has recently been an area of much theoretical and practical work univariate theory is a well documented area whereas bivariate multivariate extreme value theory has until recently received surprisingly little attention in the multivariate case no natural parametric family exists for the dependence structure so this must be modelled in some way in the analysis of environmental extreme value data there is a need for models of dependence between extremes from different sources for example at various sea ports or at various points of a river in this paper we consider bivariate extreme value distributions we assume without loss of generality that we have exponential marginal distributions with unit means the class of bivariate exponential distributions in which we are interested satisfy a strong stability relation exponential variables x y satisfy the stability relation if and only if w min ax by is also exponentially distributed for all a b 0 pickands 1981 therefore the models we will consider have particular application in reliability and survival analysis one approach to modelling the dependence structure is via parametric models this requires a flexible family of models which satisfy certain constraints models are of two kinds either differentiable or nondifferentiable all nondifferentiable models give distri butions which are singular with nonzero probability concentrated on a certain subspace the differentiable models have densities but the existing models are symmetric which leads to the variables being exchangeable here we present two new asymmetric differenti able models which have increased flexibility properties of the differentiable models are examined estimation of the parametric models has previously been by ad hoc methods because there is a nonregular estimation problem when the margins are independent for the

a specification test for univariate and multivariate proportional hazards models this paper applies white s 1982 econometrica 50 1 25 information matrix im test for correct model specification to proportional hazards models of univariate and multivariate censored survival data several alternative estimators of the test statistic are presented and their size performance examined white also suggested an estimator of the parameter covariance matrix that was robust to certain forms of model misspecification this has been subsequently proposed by others e g royall 1986 international statistical review 54 221 226 and applied by huster brookmeyer and self 1989 biometrics 45 145 156 as part of an independence working model iwm approach to multivariate censored survival data we illustrate how the im test can be used for both univariate data and as part of the iwm approach to multivariate data

heterogeneity in models for bivariate survival the importance of the mixing distribution we consider bivariate survival models in which the dependence between two survival times is by way of stochastically related unobserved components we analyse the role of the mixing or heterogeneity distribution in such models and examine how this distribution affects the correlation between the two survival times we derive explicit expressions for the correlation between the survival times and examine some properties of this correlation furthermore we examine to what extent the choice of this heterogeneity distribution is important for the hazards in the sample and their time paths the time path of the hazard among the survivors is determined by the moments of the distribution describing the heterogeneity among the survivors

a simple estimator for a shared frailty regression model summary we propose a simple estimation procedure for a proportional hazards frailty regression model for clustered survival data in which the dependence is generated by a positive stable distribution inferences for the frailty parameter can be obtained by using output from cox regression analyses the computational burden is substantially less than that of the other approaches to estimation the large sample behaviour of the estimator is studied and simulations show that the approximations are appropriate for use with realistic sample sizes the methods are motivated by studies of familial associations in the natural history of diseases their practical utility is illustrated with sib pair data from beaver dam wisconsin

on robustness of marginal regression coefficient estimates and hazard functions in multivariate survival analysis of family data when the frailty distribution is mis specified the shared frailty model is an extension of the cox model to correlated failure times and essentially a random effects model for failure time outcomes in this model the latent frailty shared by individual members in a cluster acts multiplicatively as a factor on the hazard function and is typically modelled parametrically one commonly used distribution is gamma where both shape and scale parameters are set to be the same to allow for unique identification of baseline hazard function it is popular because it is a conjugate prior and the posterior distribution possesses the same form as gamma in addition the parameter can be interpreted as a time independent cross ratio function a natural extension of odds ratio to failure time outcomes in this paper we study the effect of frailty distribution mis specification on the marginal regression estimates and hazard functions under assumed gamma distribution with an application to family studies the simulation results show that the biases are generally 10 and lower even when the true frailty distribution deviates substantially from the assumed gamma distribution this suggests that the gamma frailty model can be a practical choice in real data analyses if the regression parameters and marginal hazard function are of primary interest and individual cluster members are exchangeable with respect to their dependencies

the identifiability of the competing risks model summary this paper considers the consequences for identifiability of introducing regressors into the competing risks model of multistate duration analysis we establish conditions under which access to regressors overturns the nonidentification theorem of cox and tsiatis for both proportional and accelerated failure time models

bisimulation and divergence abstract behavioural preorders based on the idea of bisimulation but refining the notion of bisimulation equivalence and providing explicit treatments of the phenomenon of divergence in communicating systems are investigated particular study is made of these preorders in the context of milner s calculus of communicating systems the precongruence relations generated by the preorders are characterized and axiomatized on the classes of finite closed terms and sequential terms a verification technique based on the theory of the preorders is introduced and illustrated

accelerated intensity frailty model for recurrent events data in this article we propose an accelerated intensity frailty aif model for recurrent events data and derive a test for the variance of frailty in addition we develop a kernel smoothing based em algorithm for estimating regression coefficients and the baseline intensity function the variance of the resulting estimator for regression parameters is obtained by a numerical differentiation method simulation studies are conducted to evaluate the finite sample performance of the proposed estimator under practical settings and demonstrate the efficiency gain over the gehan rank estimator based on the aft model for counting process lin et al 1998 our method is further illustrated with an application to a bladder tumor recurrence data

analysis of overdispersed count data by mixtures of poisson variables and poisson processes count data often show overdispersion compared to the poisson distribution overdispersion is typically modeled by a random effect for the mean based on the gamma distribution leading to the negative binomial distribution for the count this paper considers a larger family of mixture distributions including the inverse gaussian mixture distribution it is demonstrated that it gives a significantly better fit for a data set on the frequency of epileptic seizures the same approach can be used to generate counting processes from poisson processes where the rate or the time is random a random rate corresponds to variation between patients whereas a random time corresponds to variation within patients

p calculus internal mobility and agent passing calculi abstract the calculus is a process algebra which originates from ccs and permits a natural modelling of mobility i e dynamic reconfigurations of the process linkage using communication of names previous research has shown that the calculus has a much greater expressiveness than ccs but it also has a much more complex mathematical theory the primary goal of this work is to understand the reasons for this gap another goal is to compare the expressiveness of name passing calculi i e calculi like calculus where mobility is achieved via exchange of names and that of agent passing calculi i e calculi where mobility is achieved via exchange of agents we divide the mobility mechanisms of the calculus into internal and external mobility mechanisms the study of the subcalculus which only uses internal mobility called i suggests that internal mobility is responsible for much of the expressiveness of the calculus whereas external mobility is responsible for many of the semantic complications a pleasant property of i is the full symmetry between input and output constructs internal mobility is strongly related to agent passing mobility by imposing bounds on the order of the types of i and of the higher order calculus sangiorgi 1992 we define a hierarchy of name passing calculi based on internal mobility and one of agent passing calculi we show that there is an exact correspondence in terms of expressiveness between the two hierarchies

regression in a bivariate copula model we consider a bivariate regression model in which the error structure is arbitrary except for equality of its marginal distributions two simple but inefficient approaches to analysis are i to work with the differences between the two components of each response and ii to derive working independence estimators as if the two component responses were independent the former strategy does well if the explanatory variables are balanced over pairs and the correlation between the two components of the response is high the latter strategy does well when the response correlation is low or the covariates are highly unbalanced we show that combining the two estimators can give a procedure with high efficiency in all situations we show that an adjusted marginal score can be calculated that is uncorrelated with the within pair score so that separate intrapair and interpair estimators can be calculated for bivariate normal regression models our approach is identical to the usual procedure for combining intra and interblock information in general the approaches are not the same detailed calculations and a numerical example are given for a bivariate extreme value model due to gumbel that is also important in survival analysis extensions from bivariate to general multivariate responses are indicated

modeling random effects for censored data by a multivariate normal regression model summary a normal distribution regression model with a frailty like factor to account for statistical dependence between the observed survival times is introduced this model as opposed to standard hazard based frailty models has survival times that conditional on the shared random effect have an accelerated failure time representation the dependence properties of this model are discussed and maximum likelihood estimation of the model s parameters is considered a number of examples are considered to illustrate the approach the estimated degree of dependence is comparable to other models but the present approach has the advantage that the interpretation of the random effect is simpler than in the frailty model

marginal regression models for multivariate failure time data abstract in this article we propose a general cox type regression model to formulate the marginal distributions of multivariate failure time data this model has a nested structure in that it allows different baseline hazard functions among distinct failure types and imposes a common baseline hazard function on the failure times of the same type we prove that the maximum quasi partial likelihood estimator for the vector of regression parameters under the independence working assumption is consistent and asymptotically normal with a covariance matrix for which a consistent estimator is provided furthermore we establish the uniform consistency and joint weak convergence of the aalen breslow type estimators for the cumulative baseline hazard functions and develop a resampling technique to approximate the joint distribution of these processes which enables one to make simultaneous inference about the survival functions over the time axis and across failure types finally we assess the small sample pro

joint models for multivariate longitudinal and multivariate survival data joint modeling of longitudinal and survival data is becoming increasingly essential in most cancer and aids clinical trials we propose a likelihood approach to extend both longitudinal and survival components to be multidimensional a multivariate mixed effects model is presented to explicitly capture two different sources of dependence among longitudinal measures over time as well as dependence between different variables for the survival component of the joint model we introduce a shared frailty which is assumed to have a positive stable distribution to induce correlation between failure times the proposed marginal univariate survival model which accommodates both zero and nonzero cure fractions for the time to event is then applied to each marginal survival function the proposed multivariate survival model has a proportional hazards structure for the population hazard conditionally as well as marginally when the baseline covariates are specified through a specific mechanism in addition the model is capable of dealing with survival functions with different cure rate structures the methodology is specifically applied to the international breast cancer study group ibcsg trial to investigate the relationship between quality of life disease free survival and overall survival

checking the adequacy of the gamma frailty model for multivariate failure times summary multivariate failure time data arise when the sample consists of clusters and each cluster contains several dependent failure times the semiparametric gamma frailty model vaupel manton stallard 1979 clayton 1978 oakes 1982 for multivariate failure times characterises the intracluster dependence by the gamma frailty distribution while allowing the marginal distributions to be unspecified this paper develops both graphical and numerical techniques for checking the adequacy of this model the proposed techniques are based on the posterior expectation of the frailty given the observable data two examples from genetics are provided

extending the archimedean copula methodology to model multivariate survival data grouped in clusters of variable size summary for the analysis of clustered survival data two different types of model that take the association into account are commonly used frailty models and copula models frailty models assume that conditionally on a frailty term for each cluster the hazard functions of individuals within that cluster are independent these unknown frailty terms with their imposed distribution are used to express the association between the different individuals in a cluster copula models in contrast assume that the joint survival function of the individuals within a cluster is given by a copula function evaluated in the marginal survival function of each individual it is the copula function which describes the association between the lifetimes within a cluster a major disadvantage of the present copula models over the frailty models is that the size of the different clusters must be small and equal to set up manageable estimation procedures for the different model parameters we describe a copula model for clustered survival data where the clusters are allowed to be moderate to large and varying in size by considering the class of archimedean copulas with completely monotone generator we develop both one and two stage estimators for the copula parameters furthermore we show the consistency and asymptotic normality of these estimators finally we perform a simulation study to investigate the finite sample properties of the estimators we illustrate the method on a data set containing the time to first insemination in cows with cows clustered in herds

survival models for heterogeneous populations derived from stable distributions summary a new three parameter family of distributions on the positive numbers is proposed it includes the stable distributions on the positive numbers the gamma the degenerate and the inverse gaussian distributions the family is characterized by the laplace transform from which moments convolutions infinite divisibility unimodality and other properties are derived the density is complicated but a simple saddlepoint approximation is provided weibull and gompertz distributions are naturally mixed over some of the distributions the family is natural exponential in one of the parameters the distributions are relevant for application as frailty distributions in life table methods for heterogeneous populations desirable properties of such distributions are discussed as an example survival after myocardial infarction is considered

generalized gamma frailty model in this article we present a frailty model using the generalized gamma distribution as the frailty distribution it is a power generalization of the popular gamma frailty model it also includes other frailty models such as the lognormal and weibull frailty models as special cases the flexibility of this frailty distribution makes it possible to detect a complex frailty distribution structure which may otherwise be missed due to the intractable integrals in the likelihood function and its derivatives we propose to approximate the integrals either by monte carlo simulation or by a quadrature method and then determine the maximum likelihood estimates of the parameters in the model we explore the properties of the proposed frailty model and the computation method through a simulation study the study shows that the proposed model can potentially reduce errors in the estimation and that it provides a viable alternative for correlated data the merits of proposed model are demonstrated in analysing the effects of sublingual nitroglycerin and oral isosorbide dinitrate on angina pectoris of coronary heart disease patients based on the data set in danahy et al sustained hemodynamic and antianginal effect of high dose oral isosorbide dinitrate circulation 1977 55 381 387

on the existence and decidability of unique decompositions of processes in the applied π calculus unique decomposition has been a subject of interest in process algebra for a long time for example in bpp 1 or ccs 2 3 as it provides a normal form and useful cancellation properties we provide two parallel decomposition results for subsets of the applied calculus we show that every closed normed i e with a finite shortest complete trace process p can be decomposed uniquely into prime factors p i with respect to strong labeled bisimilarity i e such that p l p 1 p n moreover we prove that closed finite processes can be decomposed uniquely with respect to weak labeled bisimilarity we also investigate whether efficient algorithms that compute the unique decompositions exist the simpler problem of deciding whether a process is in its unique decomposition form is undecidable in general in both cases due to potentially undecidable equational theories moreover we show that the unique decomposition remains undecidable even given an equational theory with a decidable word problem

multivariate survival analysis using piecewise gamma frailty in this note we propose a frailty model called piecewise gamma frailty for correlated survival data with random effects having a nested structure in frailty models a dependence function defined as a hazard ratio of one member given the failure time of another member in a unit is determined by the distributional assumptions on frailty in the piecewise gamma frailty model the nested structure of random effects or frailty allows the dependence function to vary over the time periods this model includes existing models such as the piecewise exponential model breslow 1974 biometrics 30 89 100 and the gamma frailty model clayton 1978 biometrika 65 141 151 oakes 1982 journal of the royal statistical society series b 44 414 428 as special cases a study of familial aggregation of epilepsy is used to illustrate the proposed method

gaussian class multivariate weibull distributions theory and applications in fading channels ascertaining on the suitability of the weibull distribution to model fading channels a theoretical framework for a class of multivariate weibull distributions originated from gaussian random processes is introduced and analyzed novel analytical expressions for the joint probability density function pdf moment generating function mgf and cumulative distribution function cdf are derived for the bivariate distribution of this class with not necessarily identical fading parameters and average powers two specific distributions with arbitrary number of correlated variates are considered and studied with exponential and with constant correlation where their pdfs are introduced both cases assume equal average fading powers but not necessarily identical fading parameters for the multivariate weibull distribution with exponential correlation useful corresponding formulas as for the bivariate case are derived the presented theoretical results are applied to analyze the performance of several diversity receivers employed with selection equal gain and maximal ratio combining mrc techniques operating over correlated weibull fading channels for these diversity receivers several useful performance criteria such as the moments of the output signal to noise ratio snr including average output snr and amount of fading and outage probability are analytically derived moreover the average symbol error probability for several coherent and noncoherent modulation schemes is studied using the mgf approach the proposed mathematical analysis is complemented by various evaluation results showing the effects of the fading severity as well as the fading correlation on the diversity receivers performance

semiparametric regression analysis for recurrent event interval counts this paper deals with analysis of data from longitudinal studies where the rate of a recurrent event characterizing morbidity is the primary criterion for treatment evaluation we consider clinical trials which require patients to visit their clinical center at successive scheduled times as part of follow up at each visit the patient reports the number of events that occurred since the previous visit or an examination reveals the number of accumulated events such as skin cancers the exact occurrence times of the events are unavailable and the actual patient visit times typically vary randomly about the scheduled follow up times each patient s record thus consists of a sequence of clinic visit dates event counts corresponding to the successive time intervals between clinic visits and baseline covariates we propose a semiparametric regression model extending the fully parametric model of thall 1988 biometrics 44 197 209 to estimate and test for covariate effects on the rate of events over time while also accounting for the possibly time varying nature of the underlying event rate covariate effects enter the model parametrically while the underlying time varying event rate is modelled nonparametrically the method of severini and wong 1992 annals of statistics 20 1768 1802 is used to construct asymptotically efficient estimators of the parametric component and to specify their asymptotic distribution a simulation study and application to a data set are provided

regression analysis of multivariate incomplete failure time data by modeling marginal distributions abstract many survival studies record the times to two or more distinct failures on each subject the failures may be events of different natures or may be repetitions of the same kind of event in this article we consider the regression analysis of such multivariate failure time observations each marginal distribution of the failure times is formulated by a cox proportional hazards model no specific structure of dependence among the distinct failure times on each subject is imposed the regression parameters in the cox models are estimated by maximizing the failure specific partial likelihoods the resulting estimators are shown to be asymptotically jointly normal with a covariance matrix that can be consistently estimated simultaneous inferential procedures are then proposed extensive monte carlo studies indicate that the normal approximation is adequate for practical use the new methods allow time dependent covariates missing observations and arbitrary patterns of censorship they are illustrat

two interpretations of the discrimination parameter in this paper we propose two interpretations for the discrimination parameter in the two parameter logistic model 2plm the interpretations are based on the relation between the 2plm and two stochastic models in the first interpretation the 2plm is linked to a diffusion model so that the probability of absorption equals the 2plm the discrimination parameter is the distance between the two absorbing boundaries and therefore the amount of information that has to be collected before a response to an item can be given for the second interpretation the 2plm is connected to a specific type of race model in the race model the discrimination parameter is inversely related to the dependency of the information used in the decision process extended versions of both models with person to person variability in the difficulty parameter are considered when fitted to a data set it is shown that a generalization of the race model that allows for dependency between choices and response times rts is the best fitting model

semiparametric normal transformation models for spatially correlated survival data there is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiologic studies in this article we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data this class of models assumes that survival outcomes marginally follow a cox proportional hazard model with unspecified baseline hazard and their joint distribution is obtained by transforming survival outcomes to normal random variables whose joint distribution is assumed to be multivariate normal with a spatial correlation structure a key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields we study the relationship of the spatial correlation structure of the transformed no

survival analysis 1982 1991 the second decade of the proportional hazards regression model a short review of the cox regression model and its development from publication to 1982 is given i discuss how the model is used and presented in medical journals and how improvement may be achieved finally some extensions of the model are outlined

failure time models with matched data summary holt prentice 1974 adapted the proportional hazards model to give failure time models for matched pairs in which a member of the ith pair with covariate z has hazard function aoi t exp bz with full data more efficient estimators of b can be obtained if a weibull specialization ai qt11 of the baseline hazard can be assumed but these estimators are unavailable for censored data estimates of formed by assuming the ai s are independent gamma variates are investigated the resulting estimators are found to be more efficient than those obtained by holt prentice when the ai s are in fact gamma to be available for censored data and to perform well under a number of very different unimodal alternatives to a gamma distribution for the ai s

on semi competing risks data summary we consider a variation of the competing risks problem in which a terminal event censors a non terminal event but not vice versa the joint distribution of the events is formulated via a gamma frailty model in the upper wedge where data are observable day et al 1997 with the marginal distributions unspecified an estimator for the association parameter is obtained from a concordance estimating function a novel plug in estimator for the marginal distribution of the non terminal event is shown to be uniformly consistent and to converge weakly to a gaussian process the assumptions on the joint distribution outside the upper wedge are weaker than those usually made in competing risks analyses simulations demonstrate that the methods work well with practical sample sizes the proposals are illustrated with data on morbidity and mortality in leukaemia patients

the lazy lambda calculus in a concurrency scenario abstract the use of calculus in richer settings possibly involving parallelism is examined in terms of the effect on the equivalence between terms we concentrate on abramsky s lazy calculus and we follow two directions firstly the calculus is studied within a process calculus by examining the equivalence formula induced by milner s encoding into the calculus we start from a characterization of formula presented in sangiorgi d 1992 ph d thesis we derive a few simpler operational characterisations from which we prove full abstraction w r t levy longo trees secondly we examine abramsky s applicative bisimulation when the calculus is augmented with well formed operators that is symbols equipped with reduction rules describing their behaviour in this way the maximal discrimination between pure terms i e the finest behavioural equivalence is obtained when all operators are used we prove that the presence of certain non deterministic operators is sufficient and necessary to induce it and that it coincides with the discrimination given by formula we conclude that the introduction of non determinism into the calculus is exactly what makes applicative bisimulation appropriate for reasoning about the functional terms when concurrent features are also present in the language or when they are embedded into a concurrent language

a flexible model for multivariate interval censored survival times with complex correlation structure we focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring such data are common in developmental genetics and genetic epidemiology we propose a flexible mixed probit model that deals naturally with complex but uninformative censoring the recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject specific thresholds this bypasses the requirement for the failure times to be observed as falling into non overlapping intervals the assumption of a normal age of onset distribution of the standard probit model is relaxed by embedding within it a multivariate box cox transformation whose parameters are jointly estimated with the other parameters of the model complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible the new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins the proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific

choice of conditional models in bivariate survival we consider bivariate survival problems in which interest is in the conditional distribution of one survival variable given an uncensored observation of the other the work is motivated by an analysis of time to cancer diagnosis then subsequent survival amongst a group of organ transplant recipients the effect of conditioning is illustrated for five standard bivariate models the consequences of adopting a misspecified marginal approach in which the conditioning variable is considered to be a fixed covariate are investigated

decoding choice encodings we study two encodings of the asynchronous calculus with input guarded choice into its choice free fragment one encoding is divergence free but refines the atomic commitment of choice into gradual commitment the other preserves atomicity but introduces divergence the divergent encoding is fully abstract with respect to weak bisimulation but the more natural divergence free encoding is not instead we show that it is fully abstract with respect to coupled simulation a slightly coarser but still coinductively defined equivalence that does not enforce bisimilarity of internal branching decisions the correctness proofs for the two choice encodings introduce a novel proof tech nique exploiting the properties of explicit decodings from translations to source terms

modelling marginal hazards in multivariate failure time data we consider the problem of estimating relative risk parameters in the marginal distribution of failure times from a sample in which individuals are grouped into clusters and failure times for individuals within clusters may be dependent we assume that the marginal hazard of failure for individuals within a cluster is specified by a relative risk regression model and the structure of dependence between individuals within a cluster is left unspecified estimating equations for the regression parameters and an estimator for the cumulative base line marginal hazard are described

analysis of dependent survival data applied to lifetimes of amalgam fillings when studying lifetimes of amalgam fillings one is faced with the fact that each patient may contribute multiple survival data this creates a dependence problem and some methods for solving it are presented here analysis for individual patients with simple approaches for combining data is discussed first then a parametric frailty model is applied to analyse variation within and between patients the results are presented in the form of an empirical bayes estimate for each patient

a multivariate distribution with weibull connections a simple form of multivariate distribution is defined which for certain parameter values has weibull marginals the distribution has a single parameter governing association between the variates which may be positive negative or zero the simple forms for marginal and conditional distributions hazard functions and densities make it attractive for practical application and interpretation

handling flexibility in a generalised job shop with a fuzzy approach abstract in this paper we deal with a particular scheduling problem inspired by a practical case coming from a belgian pharmaceutical company this problem has some particularities with respect to the classical job shop problem the principal one is the existence of a delay between the end of an operation and the start of the next operation of the same job this delay is not fixed but belongs generally to an interval range of possible values a resolution method the horizon method has been proposed in the deterministic case fixed data and strict constraints in this method the horizon time of each machine is discretised by unit times and at each unit time a binary value is associated all the operations applied on the machines horizons are logic operations or and right or left shifting and so on unfortunately this method does not take into account several aspects of the problem the existence of a preference relation on the possible values of the delay between successive operations and of flexible due dates in this work we modelise such flexible parameters using the fuzzy logic we propose a new method generalizing the horizon method this new method try to find for the realisation of the jobs a compromise between the choice of good values for the delay between couples of successive operations of the same job and good values for the completion time of the jobs

a bivariate cure mixture approach for modeling familial association in diseases summary for modeling correlation in familial diseases with variable ages at onset we propose a bivariate model that incorporates two types of pairwise association one between the lifetime risk or the overall susceptibility of two individuals and one between the ages at onset between two susceptible individuals for estimation we consider a two stage estimation procedure similar to that of shih 1998 biometrics 54 1115 1128 we evaluate the properties of the estimators through simulations and compare the performance with that from a bivariate survival model that allows correlation between ages at onset only we apply the methodology to breast cancer using the kinship data from the washington ashkenazi study we also discuss potential applications of the proposed method in the area of cure modeling

pairwise dependence diagnostics for clustered failure time data frailty and copula models specify a parametric dependence structure for multivariate failure time data estimation of some joint quantities can be highly sensitive to the assumed parametric form and hence model fit is an important issue this paper lays out a general diagnostic framework for evaluating and selecting frailty and copula models the approach is based on the cumulative sum of residuals that are calculated in bivariate time the residuals reflect the difference between the observed and expected bivariate association structures the proposed model checking process is interpretable with a limiting distribution which can be approximated using the bootstrap simulations and a data example illustrate the practical application of the method

a random effects model for multivariate failure time data from multicenter clinical trials a random effects model for analyzing multivariate failure time data is proposed the work is motivated by the need for assessing the mean treatment effect in a multicenter clinical trial study assuming that the centers are a random sample from an underlying population an estimating equation for the mean hazard ratio parameter is proposed the proposed estimator is shown to be consistent and asymptotically normally distributed a variance estimator based on large sample theory is proposed simulation results indicate that the proposed estimator performs well in finite samples the proposed variance estimator effectively corrects the bias of the naive variance estimator which assumes independence of individuals within a group the methodology is illustrated with a clinical trial data set from the studies of left ventricular dysfunction this shows that the variability of the treatment effect is higher than found by means of simpler models

towards a unified approach to encodability and separation results for process calculi we present a unified approach to evaluate the relative expressive power of process calculi in particular we identify a small set of criteria that have already been somehow presented in the literature that an encoding should satisfy to be considered a valid means for language comparison we argue that the combination of such criteria is a valid proposal by noting that i several well known encodings appeared in the literature satisfy them ii this notion is not trivial because some known encodings do not satisfy all the criteria we have proposed iii several well known separation results can be formulated in terms of our criteria and iv some widely believed but never formally proved separation results can be proved by using the criteria we propose moreover the criteria defined induce general proof techniques for separation results that can be easily instantiated to cover known case studies

statistical analysis of repeated events forming renewal processes for each of several individuals a sequence of repeated events forming a renewal process is observed up to some censoring time the object is to estimate the average interevent time over the population of individuals as well as the variation of interevent times within and between individuals medical motivation comes from gastroenterology and concerns the occurrence of certain cyclic movements in the small bowel during the fasting state two statistical models are considered one is the standard variance component model adapted to censored data and the other is a recent intensity based model with a random proportionality factor representing interindividual variation these models are applied to the motility data and their advantages are discussed the intensity based model allows simple empirical bayes estimation of the expected interevent times for an individual in the presence of censoring

methods for bounding the marginal survival distribution for time to event data with many potential failure types one cannot uniquely determine the distribution of time to a specific event type or marginal survival distribution in the case where event types are mutually exclusive in this paper we discuss several methods for estimating functions that bound the non identifiable marginal survival distribution in the competing risks problem we compute and compare bounds for data simulated from two bivariate survival distributions results show that the methods provide a suitable estimate of the marginal survival probability when one has specified dependence correctly data from a large clinical trial for breast cancer illustrate the methods

theory of interaction abstract theory of interaction aims to provide a foundational framework for computation and interaction it proposes four fundamental principles that characterize the common features of all models of computation and interaction these principles suffice to support a model independent treatment of the two most important relationships in computer science the equality between processes and the relative expressiveness between models based on the two relationships the theory of equality the theory of expressiveness and the theory of completeness are developed

jobshop scheduling with imprecise durations a fuzzy approach jobshop scheduling problems are np hard problems the durations in the reality of manufacturing are often imprecise and the imprecision in data is very critical for the scheduling procedures therefore the fuzzy approach in the framework of the dempster shafer theory commands attention the fuzzy numbers are considered as sets of possible probabilistic distributions after a review of some issues concerning fuzzy numbers we discuss the determination of a unique optimal solution of the problem and then we cast a meta heuristic simulated annealing sa to this particular framework for optimization it should be stressed that the obtained schedule remains feasible for all realizations of the operations durations

a fast taboo search algorithm for the job shop problem a fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a job shop is presented the algorithm is based on a taboo search technique with a specific neighborhood definition which employs a critical path and blocks of operations notions computational experiments up to 2 000 operations show that the algorithm not only finds shorter makespans than the best approximation approaches but also runs in shorter time it solves the well known 10 10 hard benchmark problem within 30 seconds on a personal computer

analysing bivariate survival data with interval sampling and application to cancer epidemiology in biomedical studies ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease in cancer studies interest could be focused on bivariate failure times for example time from birth to cancer onset and time from cancer onset to death this paper considers a sampling scheme termed interval sampling in which the first failure event is identified within a calendar time interval the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring in a cancer data application the initiating first and second events could correspond to birth cancer onset and death the fact that the data are collected conditional on the first failure event occurring within a time interval induces bias interval sampling is widely used for collection of disease registry data by governments and medical institutions though the interval sampling bias is frequently overlooked by researchers this paper develops statistical methods for analysing such data semiparametric methods are proposed under semi stationarity and stationarity numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes we apply the proposed methods to ovarian cancer registry data

a model for association in bivariate survival data summary a reparameterization of a model introduced by d g clayton for association in bivariate life tables is discussed inference for the parameter governing the association is con sidered when the marginal distributions are specified up to lehmann alternatives the information matrix is derived explicitly and it is shown that the parameterization is moderately successful in introducing orthogonality between the association parameter and the two scale parameters the likelihood proposed by clayton for the case that the marginal distributions are completely unknown is criticized an alternative non parametric estimator based on kendall s coefficient of concordance is proposed and its asymptotic variance evaluated if g s pr s s and h t pr t t are continuous univariate survivor functions with g o h o 1 and 0 1 then the function f s t pr s s t t given by f s t g h 1 1 is a bivariate survivor function with g and h as marginals this paper considers inference about the parameter 0 governing the association between s and t given a sample of size n from their bivariate distribution we suppose first that the marginals g s and h t are known up to lehmann alternatives and secondly that they are completely unknown the model 1 is a reparameterization of a model introduced by clayton 1978 for association in bivariate life tables section 2 describes some properties of the family including the limits as 0 1 and 0 oo a simple interpretation of 0 in terms of hazard functions and a random effects derivation of the model this section leans heavily on clayton s work section 3 discusses parametric inference for 0 including tests of the hypothesis 0 1 of independence between s and t we assume that the marginal survivor functions g s and h t are specified up to lehmann alternatives asymptotic variance formulae are derived in section 4 which also explores the extent to which the parameterization achieves orthogonality clayton 1978 proposed an ingenious method for estimating 0 in the absence of knowledge of the marginal distributions unfortunately there are difficulties with his proposed con ditional likelihood function in particular the estimated variance from the second derivative appears to be incorrect clayton s derivation is described and criticized in our section 5 in principle rank invariance properties of 1 when g s and h t are completely unknown yield a marginal likelihood for 0 although this likelihood appears to be intractable in general in section 6 it is evaluated when n 3 and shown to differ from clayton s likelihood a simpler non parametric estimator of 0 based on the coefficient of concordance is proposed in section

general local search methods abstract this paper is a tutorial introduction to three recent yet widely used general heuristics simulated annealing tabu search and genetic algorithms a relatively precise description and an example of application are provided for each of the methods as well as a tentative evaluation and comparison from a pragmatic point of view

cumulative incidence association models for bivariate competing risks data association models like frailty and copula models are frequently used to analyze clustered survival data and evaluate within cluster associations the assumption of noninformative censoring is commonly applied to these models though it may not be true in many situations in this paper we consider bivariate competing risk data and focus on association models specified for the bivariate cumulative incidence function cif a nonparametrically identifiable quantity copula models are proposed which relate the bivariate cif to its corresponding univariate cifs similarly to independently right censored data and accommodate frailty models for the bivariate cif two estimating equations are developed to estimate the association parameter permitting the univariate cifs to be estimated either parametrically or nonparametrically goodness of fit tests are presented for formally evaluating the parametric models both estimators perform well with moderate sample sizes in simulation studies the practical use of the methodology is illustrated in an analysis of dementia associations

biometrika centenary survival analysis although papers relating to survival analysis have appeared in biometrika since the very first issue the current emphasis on the topic in this journal is fairly new especially as it relates to applications in biostatistics this survey traces the development of survival analysis throughout the twentieth century as reflected in the pages of biometrika focusing primarily on work published since 1980

competing risks with frailty models when treatment affects only one failure type summary using a model in which treatment affects only the failure type of primary clinical interest the use of a cause specific analysis is examined when the latent failure times are derived from a frailty model the relationship between the cause specific hazard ratio and the marginal hazard ratio is expressed in terms of a local dependence measure of the latent failure times frequently in clinical trials the treatment being tested is expected to affect only one of several clinical outcomes for example radiation treatment to a tumour site may affect the local but not distant recurrence rates adjuvant cancer chemotherapy given to an elderly population may affect the probability of dying from cancer but not from other causes competing risks problems arise when occurrence of a secondary outcome makes the subsequent occurrence of the primary outcome unobservable or uninterpretable for reviews of various approaches to the analysis of survival data with competing risks see gail 1975 or prentice et al 1978 one approach postulates latent failure times for each possible outcome the minimum of these latent failure times and an indicator of failure type are observed possible parameters of interest include the marginal survival curve or the crude cumulative incidence curve associated with the latent failure type of primary interest and the survival curve associated with the minimum of latent failure times the choice of parameter should depend on the clinical question korn dorey 1992 since we will assume that the treatment affects only the latent failure times associated with the primary outcome we concentrate on inference for the marginal survival distribution of that latent failure time however as is well known peterson 1976 the marginal survival distribution associated with a latent failure time is not identifiable without further assumptions frequently a cause specific survival curve for the primary failure type is constructed by treating failures of other types as censored observations if the latent failure times are statistically independent then the cause specific survival curve will estimate the marginal survival curve in this paper we examine one implication of performing a cause specific analysis of a treatment when the latent failure times are not independent in particular for two types of failure we will compare the cause specific hazard ratio with the marginal hazard ratio for the primary failure type of interest our underlying model will specify that the treatment affects only the primary latent failure times we allow for non independence of the latent failure times by specifying their bivariate distribution in terms of an archimedean copula genest mackay 1986 these bivariate distri butions subsume the frailty models of clayton 1978 and hougaard 1984 1986 see oakes 1989 a general approach to identifiable marginal survival using a specified copula is given by zheng

estimation of variance in cox s regression model with shared gamma frailties the cox regression model with a shared frailty factor allows for unobserved heterogeneity or for statistical dependence between the observed survival times estimation in this model when the frailties are assumed to follow a gamma distribution is reviewed and we address the problem of obtaining variance estimates for regression coefficients frailty parameter and cumulative baseline hazards using the observed nonparametric information matrix a number of examples are given comparing this approach with fully parametric inference in models with piecewise constant baseline hazards

on use of bivariate survival models with cure fraction correlated gamma frailty model in our original article we proposed the methodology in the very general setting of copula model genest and mackay 1986 marshall and olkin 1988 we illustrated the application of the methodology using three specific distribution of the general class namely clayton s 1978 frank s 1979 and stable family hougaard 1986 it is true that all of these three models correspond to an underlying shared frailty model the class of copula models however are much more general and can include correlated frailty models in fact the correlated frailty model the authors proposed to use as an extension of our methodology is a special case of the copula model with the copula function given by

bivariate location scale models for regression analysis with applications to lifetime data summary the literature on multivariate linear regression includes multivariate normal models models that are used in survival analysis and a variety of models that are used in other areas such as econometrics the paper considers the class of location scale models which includes a large proportion of the preceding models it is shown that for complete data the maximum likelihood estimators for regression coefficients in a linear location scale framework are consistent even when the joint distribution is misspecified in addition gains in efficiency arising from the use of a bivariate model as opposed to separate univariate models are studied a major area of application for multivariate regression models is to clustered parallel lifetime data so we also study the case of censored responses estimators of regression coefficients are no longer consistent under model misspecification but we give simulation results that show that the bias is small in many practical situations gains in efficiency from bivariate models are also examined in the censored data setting the methodology in the paper is illustrated by using lifetime data from the diabetic retinopathy study

a general class of parametric models for recurrent event data the general class of models proposed by pena and hollander for recurrent event data is considered under a fully parametric specification of the baseline hazard rate function and under the two cases where the model does and does not incorporate frailty components estimators of model parameters are presented and their finite and asymptotic properties are ascertained for the asymptotic properties the results of borgan concerning maximum likelihood estimators in counting process models are used to obtain weak convergence to gaussian distributions of estimators however the required regularity conditions are reformulated into conditions involving gap times which make it more feasible to obtain explicit theoretical expressions of asymptotic covariances the procedures are applied to fit the general class of models with a parametric baseline hazard rate function to a dataset on hydraulic subsystems of load haul dump machines in mining

bivariate survival models induced by frailties abstract when two observed survival times depend via a proportional hazards model on the same unobserved variable called a frailty this common dependence induces an association between the observed times this article considers the class of bivariate survival distributions that can arise in this way extends it to allow negative association and shows that the observable bivariate distribution determines the unobserved frailty distribution up to a scale parameter a cross ratio function easily estimated even from censored data is the key to both the characterization results and inferential procedures and diagnostic plots that are introduced and illustrated by real examples the models considered are the natural counterpart for survival data of the latent variable models that have long been used in factor analysis for continuous data or in latent structure analysis for binary data the main distinguishing feature of survival data is the possibility of censoring which makes the standard methods difficu

what is a good encoding of guarded choice abstract the calculus with synchronous output and mixed guarded choices is strictly more expressive than the calculus with asynchronous output and no choice this result was recently proved by c palamidessi and as a corollary she showed that there is no fully compositional encoding from the former into the latter that preserves divergence freedom and symmetries this paper argues that there are nevertheless good encodings between these calculi in detail we present a series of encodings for languages with 1 input guarded choice 2 both input and output guarded choice and 3 mixed guarded choice and investigate them with respect to compositionality and divergence freedom the first and second encoding satisfy all of the above criteria but various good candidates for the third encoding inspired by an existing distributed implementation invalidate one or the other criterion while essentially confirming palamidessi s result our study suggests that the combination of strong compositionality and divergence freedom is too strong for more practical purposes

semiparametric estimation of random effects using the cox model based on the em algorithm consider a survival experiment where individuals within a certain subset of the population share a common unobservable random frailty such a frailty could be an unobservable genetic or early environmental effect if individuals were in sibling groups or an environmental effect if individuals were grouped by households suppose that if the frailty omega is known the cox proportional hazards model for the observable covariates is valid with the consequence of the random effect being a multiplicative factor on the hazard rate assuming tht the random frailties follow a gamma distribution estimates of the fixed and random effects are obtained by using an em algorithm based on a profile likelihood construction the method developed is applied to the framingham heart study to examine the risks of smoking and cholesterol levels adjusting for potential random effects

copulas based time series combined forecasters time series combined forecasters have been superior to the respective single models in statistical terms in this way the linear combination functions e g the simple average sa and the minimal variance mv approaches have been the main alternatives for aggregation in the literature in this work it is presented a copulas based method for combining biased single models copulas are multivariate functions that operate on marginal probability distributions and have the specific advantage of generalizing mv by flexibly modelling the forecasters residuals and then the dependence among them a typical divide and conquer framework that can result in superior combined forecasters the usefulness of the copulas based combination method is highlighted by means of a comparison with sa and mv models based on a number of simulated cases and a real world time series

on the expressive power of movement and restriction in pure mobile ambients pure mobile ambients is a process calculus suitable to focus on issues related to mobility abstracting away from aspects concerning process communication however it incorporates name restriction i e the vn binder and ambient movement i e the in and out capabilities that can be seen as characteristics adapted or directly borrowed from the tradition of communication based process calculi for this reason we retain that it is worth to investigate whether or not these features can be removed from pure mobile ambients without losing expressive power to this aim we consider two variants of pure mobile ambients which differ in the way infinite processes can be defined the former exploits process replication while the latter is more general and permits recursive process definition we analyse whether or not the elimination of ambient movement and or name restriction reduces the expressive power of these two calculi using the decidability of process termination as a yardstick we prove that name restriction can be removed from both calculi without reducing the expressive power on the other hand the elimination of both ambient movement and name restriction strictly reduces the expressive power of both calculi as far as the elimination of only ambient movement is concerned we prove an interesting discrimination result process termination is undecidable under recursive process definition while it turns out to be decidable under process replication

job sequencing with fuzzy processing times abstract in practice processing times can be more accurately represented as intervals with the most probable completion time somewhere near the middle of the interval a fuzzy number which is essentially a generalized interval can represent this processing time interval exactly and naturally in this work triangular and trapezoidal fuzzy numbers are used to represent those vague job processing times in job shop production systems the job sequencing algorithms of johnson and ignall and schrage are modified to accept fuzzy job processing times fuzzy makespans and fuzzy mean flow times are then calculated for greater decision making information numerous examples are used to illustrate the approach

a class of multivanate failure time distributions summary a class of continuous multivariate lifetime distributions is proposed the dependence between individuals in a group is modelled by a group specific quantity which can be interpreted as an unobserved covariate common to the individuals in the group and assumed to follow a positive stable distribution it is possible to include covariates in the model and discuss whether the dependence is still present after specific covariates are taken into account if the conditional hazards are proportional then the hazards in the marginal distributions are also proportional but with different constants of proportionality also the hazard for the minimum in a group is proportional to the marginal hazards if the conditional distributions given the group quantity are weibull then the marginal distributions are also weibull this class can be used to test the hypothesis of independence of litter mates in the proportional hazards model

functional association models for multivariate survival processes we consider multivariate temporal processes that are continuously observed within overlapping time windows the intended application is in censored multistate and multivariate survival settings where point processes are continuously observed these data differ from other functional data like longitudinal data which are discretely observed at irregular times existing functional approaches to survival processes use intensity models which require smoothing and depend critically on the choice of smoothing parameters similarly to discretely observed data in this article we study functional mean and association regression models for the point processes with unspecified time varying coefficients the continuous observation scheme is exploited the coefficients may be estimated nonparametrically by extending generalized estimating equations to continuously observed data the estimators automatically converge at the parametric rate without smoothing unlike with discretely observed data uniform consistenc

a randomized encoding of the π calculus with mixed choice we consider the problem of encoding the calculus with mixed choice i e input output guarded choice into the asynchronous calculus we are interested in a translation which satisfies certain desirable properties namely uniformity and preservation of a reasonable semantics although it has been shown that this is not possible with an exact encoding we suggest a randomized approach using a probabilistic extension of the asynchronous calculus and we show that our solution is correct with probability 1 under any proper adversary wrt a notion of testing semantics this result establishes the basis for a distributed and symmetric implementation of mixed choice which unlike previous proposals in literature does not rely on assumptions on the relative speed of processes and it is robust to attacks of proper adversaries

time dependent association measures for bivariate survival distributions abstract we propose time dependent association measures for application to bivariate survival analysis such association measures provide informative summaries for data on twins ophthalmic and auditory studies and for other matched pair designs we develop several desirable properties of time dependent association measures and study three measures motivated by these properties we examine the measures from a general bivariate survival perspective and for the proportional hazards frailty model we use monozygotic mz and dizygotic dz twin data from the danish twin registry to illustrate how these measures depend on the specification of the proportional hazards frailty model this model consists of two components a baseline hazard function and a frailty distribution we produce gamma and nonparametric maximum likelihood estimates of the frailty distribution and estimate a gompertz baseline hazard function for two of the measures a nonparametric estimate provides a comparison to the model based estim

model selection and semiparametric inference for bivariate failure time data abstract we propose model selection procedures for bivariate survival models for censored data generated by the archimedean copula family in route to constructing the selection methodology we develop estimates of some time dependent association measures including estimates of the local and global kendall s tau local odds ratio and other measures defined throughout the literature we propose a goodness of fit based model selection methodology as well as a graphical approach we show that the proposed methods have desirable asymptotic properties and perform well in finite samples

multivariate survival analysis with positive stable frailties in this paper we describe bayesian modeling of dependent multivariate survival data using positive stable frailty distributions a flexible baseline hazard formulation using a piecewise exponential model with a correlated prior process is used the estimation of the stable law parameter together with the parameters of the conditional proportional hazards model is facilitated by a modified gibbs sampling procedure the methodology is illustrated on kidney infection data mcgilchrist and aisbett 1991

on consistency of kendall s tau under censoring necessary and sufficient conditions for consistency of a simple estimator of kendall s tau under bivariate censoring are presented the results are extended to data subject to bivariate left truncation as well as right censoring

multivariate hydrological frequency analysis using copulas 1 this article presents the modeling of multivariate extreme values using copulas our approach allows us to model the dependence structure independently of the marginal distributions which is not possible with standard classical methods the methodology has been applied on two different problems in hydrology the first application is concerned with the combined risk in the framework of frequency analysis four copulas have been tested on peak flows from the watershed of peribonka in quebec canada the second application relates to the joint modeling of peak flows and volumes three copulas have been applied to the watershed of the rimouski river in quebec canada this approach using copulas is promising since it allows us to take into account a wide range of correlation which can happen in hydrology

modelling multivariate failure time associations in the presence of a competing risk there has been much research on analysing multivariate failure times but little that has accommodated failures that arise in the presence of a competing failure process this paper studies the problem of describing associations among times to such failures it proposes a modified conditional hazard ratio measure of association that is tailored to competing risks data develops frailty models and a nonparametric method for describing the proposed measure and contrasts estimation by proposed methods with the standard of treating competing risks as independently censoring failure times due to targeted causes the methods are investigated on simulated and real data

life table methods for heterogeneous populations distributions describing the heterogeneity taking account of heterogeneity between the individuals in population based mortality studies is important a systematic way of describing heterogeneity is by an unobserved quantity called frailty entering the hazard multiplicatively until now most studies have used a gamma distributed frailty which is mathematically convenient for example the distribution among survivors is also gamma this paper shows that several other distributions have equally simple properties the main example being the inverse gaussian distribution consequences of the different distributions are examined the inverse gaussian makes the population homogeneous with time whereas for the gamma the relative heterogeneity is constant

some efficient heuristic methods for the flow shop sequencing problem abstract in this paper the best heuristic methods known up to now are compared to solve the flow shop sequencing problem and we improve the complexity of the best one next this problem is applied to taboo search a new technique to solve combinatorial optimization problems and computational experiments are reported finally a parallel taboo search algorithm is presented and experimental results show that this heuristic allows very good speed up