a large sample study of cox s regression model strong consistency and asymptotic normality are established for the maximum partial likelihood estimate of the regression parameter in cox s regression model estimates are also derived for the underlying cumulative hazard function and survival distribution we establish the asymptotic normality of these estimates and calculate the limiting variances

cox s regression model for counting processes a large sample study the cox regression model for censored survival data specifies that covariates have a proportional effect on the hazard function of the life time distribution of an individual in this paper we discuss how this model can be extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process this permits a statistical regression analysis of the intensity of a recurrent event allowing for complicated censoring patterns and time dependent covariates furthermore this formulation gives rise to proofs with very simple structure using martingale techniques for the asymptotic properties of the estimators from such a model finally an example of a statistical analysis is included

on switching h controllers for a class of linear parameter varying systems abstract we consider switching h controllers for a class of linear parameter varying lpv systems scheduled along a measurable parameter trajectory the candidate controllers are selected from a given controller set according to the switching rules based on the scheduling variable we provide sufficient conditions to guarantee the stability of the switching lpv systems in terms of the dwell time and the average dwell time our results are illustrated with an example where switching between two robust controllers is performed for an lpv system

formulae for the derivatives of heat semigroups abstract formulae for the derivatives of solutions of diffusion equations are derived which clearly exhibit and allow estimation of the equations smoothing properties these also give formulae for the logarithmic gradient of the corresponding heat kernels extending and giving a very elementary proof of bismut s well known formula corresponding formulae are derived for solutions of heat equations for differential forms and their exterior derivatives

stability analysis for neutral stochastic delay systems with markovian switching abstract this paper considers the exponential stability in p th p 1 moment and the almost sure exponential stability for neutral stochastic delay systems with markovian switching by using the generalized integral inequality and the nonnegative local martingale convergence theorem the lyapunov stability results on the exponential stability in p th p 1 moment and the almost sure exponential stability for such systems are given when the time varying delay is a bounded measurable function and the coefficients in the monotonicity condition are time varying the derived results are illustrated by two examples

estimates of the derivatives for parabolic operators with unbounded coefficients we consider a class of second order uniformly elliptic operators a with unbounded coefficients in r n using a bernstein approach we provide several uniform estimates for the semigroup t t generated by the realization of the operator a in the space of all bounded and continuous or holder continuous functions in r n as a consequence we obtain optimal schauder estimates for the solution to both the elliptic equation u au f 0 and the nonhomogeneous dirichlet cauchy problem d t u au g then we prove two different kinds of pointwise estimates of t t that can be used to prove a liouville type theorem finally we provide sharp estimates of the semigroup t t in weighted l p spaces related to the invariant measure associated with the semigroup

partially linear hazard regression with varying coefficients for multivariate survival data summary the paper studies estimation of partially linear hazard regression models with varying coefficients for multivariate survival data a profile pseudo partial likelihood estimation method is proposed the estimation of the parameters of the linear part is accomplished via maximization of the profile pseudo partial likelihood whereas the varying coefficient functions are considered as nuisance parameters that are profiled out of the likelihood it is shown that the estimators of the parameters are root n consistent and the estimators of the non parametric coefficient func tions achieve optimal convergence rates asymptotic normality is obtained for the estimators of the finite parameters and varying coefficient functions consistent estimators of the asymptotic variances are derived and empirically tested which facilitate inference for the model we prove that the varying coefficient functions can be estimated as well as if the parametric components were known and the failure times within each subject were independent simulations are con ducted to demonstrate the performance of the estimators proposed a real data set is analysed to illustrate the methodology proposed

quasi factorization of the entropy and logarithmic sobolev inequalities for gibbs random fields we show that the entropy functional exhibits a quasi factorization property with respect to a pair of weakly dependent algebras as an application we give a simple proof that the dobrushin and shlosmans complete analyticity condition for a gibbs specification with finite range summable interaction implies uniform logarithmic sobolev inequalities this result has been previously proven using several different techniques the advantage of our approach is that it relies almost entirely on a general property of the entropy while very little is assumed on the dirichlet form no topology is introduced on the single spin space thus discrete and continuous spins can be treated in the same way

a nonparametric method for dealing with mismeasured covariate data abstract mismeasurement of covariate data is a frequent problem in statistical data analysis however when true and mismeasured data are obtained for a subsample of the observations it is possible to estimate the parameters relating the outcome to the covariate of interest maximum likelihood methods that rely on parametric models for the mismeasurement have not met with much success realistic models for the mismeasurement process are difficult to construct the form of the likelihood is often intractable and more important such methods are not robust to model misspecification we propose an easily implemented method that is nonparametric with respect to the mismeasurement process and that is applicable when mismeasurement is due to the problem of missing data errors in variables or use of imperfect surrogate covariates specifically denote the outcome variable by y the covariate data subject to mismeasurement by x and the remaining covariates including perhaps surrogates or mismeasured values

l_ 2 l_ infty model reduction for switched lpv systems with average dwell time in this note the model reduction problem for a class of discrete time switched linear parameter varying systems under average dwell time switching is investigated a parameterized reduced model is constructed and the corresponding existence conditions of such models are derived via strict lmi formulation the minimal average dwell time among all the subsystems and the desired reduced system are obtained such that the resulting model error system is exponentially stable and has a guaranteed l 2 l infin error performance a numerical example is given to demonstrate the potential and effectiveness of the developed theoretical results

inference on endogenously censored regression models using conditional moment inequalities under a quantile restriction randomly censored regression models can be written in terms of conditional moment inequalities we study the identified features of these moment inequalities with respect to the regression parameters where we allow for covariate dependent censoring endogenous censoring and endogenous regressors these inequalities restrict the parameters to a set we show regular point identification can be achieved under a set of interpretable sufficient conditions we then provide a simple way to convert conditional moment inequalities into unconditional ones while preserving the informational content our method obviates the need for nonparametric estimation which would require the selection of smoothing parameters and trimming procedures without the point identification conditions our objective function can be used to do inference on the partially identified parameter maintaining the point identification conditions we propose a quantile minimum distance estimator which converges at the parametric rate to the parameter vector of interest and has an asymptotically normal distribution a small scale simulation study and an application using drug relapse data demonstrate satisfactory finite sample performance

stratified partial likelihood estimation when multiple durations are generated by a single unit they may be related in a way that is not fully captured by the regressors the omitted unit specific variables might vary over the durations they might also be correlated with the variables in the regression component we propose an estimator that responds to these concerns and develop a specification test for detecting unobserved unit specific effects data from malaysia reveal that concentration of child mortality in some families is imperfectly explained by observed explanatory variables and that failure to control for unobserved heterogeneity seriously biases the parameter estimates

brief paper dynamic output feedback h control of switched linear systems this paper is devoted to dynamic output feedback h control design of switched linear systems in both continuous and discrete time domains more specifically the main purpose is to jointly design a switching rule and a full order dynamic output feedback switched controller that render the associated closed loop switched linear system globally asymptotically stable and impose a pre specified upper bound to the l 2 gain an example of practical importance is presented to illustrate the validity and efficiency of the theory

event triggered h filtering of markov jump systems with general transition probabilities abstract this paper investigates the h filtering of markov jump systems with general transition probabilities which are known uncertain and unknown the transmission from sensor to filter is determined by a mode dependent event triggered scheme the advantage of the scheme is that no restriction is imposed on a triggering scalar employing a zero order holder the resulting filtering error system is expressed by markov jump systems with time delay with the aid of a relaxed lyapunov krasovskii functional and the finlser lemma the desired h filter gains and the related triggering parameter are solved in a uniformed framework it is shown that the proposed approach is less conservative than the existing one numerical examples are given to verify the validity of the proposed method

gradient estimates for dirichlet parabolic problems in unbounded domains we consider autonomous parabolic dirichlet problems in a regular unbounded open set rn involving second order operator a with possibly unbounded coefficients we determine new conditions on the coefficients of a yielding global gradient estimates for the bounded classical solution

on stability l2 gain and h control for switched systems this paper addresses the issues of stability l 2 gain analysis and h control for switched systems via multiple lyapunov function methods a concept of general lyapunov like functions is presented a necessary and sufficient condition for stability of switched systems is given in terms of multiple generalized lyapunov like functions which enables derivation of improved stability tests an l 2 gain characterization and a design method for stabilizing switching laws a solution to the h control problem for switched systems is also provided

flood frequency analysis using the cox regression model procedures for incorporating time varying exogenous information into flood frequency analyses are developed using the cox regression model for counting processes in this statistical model the probability of occurrence of a flood peak in a short interval t t dt depends in an explicit manner on the values at t of k covariate processes z1 zk specifically letting dn t be 1 if a flood peak occurs in t t dt and 0 otherwise dn t a t exp j 1kbjzj t dm t where a the baseline intensity is an unknown function b is a vector of unknown regression parameters and the error dm t is conditionally orthogonal to the past history two applications assessment of relative importance of physical processes such as snow melt or soil moisture storage on flood frequency at a site and derivation of time varying flood frequency estimates are considered

stability and stabilization of switched linear systems with mode dependent average dwell time in this paper the stability and stabilization problems for a class of switched linear systems with mode dependent average dwell time mdadt are investigated in both continuous time and discrete time contexts the proposed switching law is more applicable in practice than the average dwell time adt switching in which each mode in the underlying system has its own adt the stability criteria for switched systems with mdadt in nonlinear setting are firstly derived by which the conditions for stability and stabilization for linear systems are also presented a numerical example is given to show the validity and potential of the developed techniques

on the parabolic kernel of the schrodinger operator etude des equations paraboliques du type q x t t u x t 0 sur une variete riemannienne generale introduction estimations de gradients inegalites de harnack majorations et minorations des solutions fondamentales equation de la chaleur et noyau de green operateur de schrodinger

coupling of brownian motions and perelman s l functional abstract we show that on a manifold whose riemannian metric evolves under backwards ricci flow two brownian motions can be coupled in such a way that their normalized l distance is a supermartingale as a corollary we obtain the monotonicity of the transportation cost between two solutions of the heat equation in the case that the cost function is the composition of a concave non decreasing function and the normalized l distance in particular it provides a new proof of a recent result of topping p topping l optimal transportation for ricci flow j reine angew math 636 2009 93 122

the l 1 method for robust nonparametric regression abstract consider the problem of estimating the regression function underlying a set of data that is contaminated by a heavy tailed error distribution the two standard approaches to such a problem are each flawed robust parametric least squares is appropriate only if there is good reason to believe that the underlying function has some particular form whereas most nonparametric regression methods are asymptotically equivalent to kernel regression methods which are not resistant against outliers existing algorithms for robustifying nonparametric regression procedures use either nonlinear optimization of an influence function or iterative solution of local polynomial fitting using reweighted least squares neither of these two approaches combines computational ease with asymptotic theoretical results furthermore application of the robust procedure has been limited almost exclusively to the case of a single explanatory variable with the response variable in this article a new hybrid method is propose

gradient estimates on manifolds using coupling we use a coupling method to give gradient estimates for solutions to 12 z u 0 on a manifold the size of the gradient depends on a lower bound on the ricci curvature of the manifold and bounds on the vector field z

duration dependence and nonparametric heterogeneity a monte carlo study we examine the behaviour of the nonparametric maximum likelihood estimator npmle for a discrete duration model with unobserved heterogeneity and unknown duration dependence we find that a nonparametric specification of either the duration dependence or unobserved heterogeneity when the other feature of the hazard is known to be absent leads to estimators that are well behaved even in modestly sized samples in contrast there is a large and systematic bias in the parameters of these components when both are specified nonparametrically as well as a complementary bias in the coefficients on observed heterogeneity furthermore these biases diminish very gradually as sample size increases we find that a minor modification of the quasilikelihood that penalizes specifications with many points of support leads to a dramatic improvement

external stability of switching control systems abstract this paper investigates external stability defined by the normal l 2 norm of switching control systems it proposes definitions of the maximum minimum dwell time for switching systems and then derives an important relation between the number of switchings and the maximum minimum dwell time applying this relation it establishes criteria on external stability of switching control systems consisting of hurwitz stable subsystems furthermore a switching law is proposed and is proven to be realizable given this proposed switching law and applying the previous derived relation the switching control systems comprised of both hurwitz stable and hurwitz unstable subsystems are proved to be externally stable

finite time stability and stabilization for ito type stochastic markovian jump systems with generally uncertain transition rates abstract this paper investigates the problems of finite time stability and stabilization for stochastic markovian jump systems with generally uncertain transition rates smjswgutrs firstly a less conservative stability criterion is presented by a mode dependent approach then the state feedback controller and observer based controller are designed by a cone complementary linearization method and two specific algorithms are presented finally an example is used to illustrate our results

barrier lyapunov functions for the output tracking control of constrained nonlinear switched systems abstract in this paper we investigate the output tracking control problem of constrained nonlinear switched systems in lower triangular form first when all the states are subjected to constraints we employ a barrier lyapunov function blf which grows to infinity whenever its arguments approach some finite limits to prevent the states from violating the constraints based on the simultaneous domination assumption we design a continuous feedback controller for the switched system which guarantees that asymptotic output tracking is achieved without transgression of the constraints and all closed loop signals remain bounded provided that the initial states are feasible then we further consider the case of asymmetric time varying output constraints by constructing an appropriate blf finally the effectiveness of the proposed results is demonstrated with a numerical example

stochastic incremental h control for discrete time switched systems with disturbance dependent noise abstract in this paper the reverse mode dependent average dwell time reverse mdadt scheme incorporated with multiple incremental lyapunov functions are utilized to investigate the incremental h control for nonlinear discrete time stochastic switched systems sss the sufficient conditions in terms of reverse mdadt for the incremental h control are derived such that an incrementally globally asymptotically stable in the mean igasim system with a prescribed h disturbance attenuation level is obtained it is shown that an incremental h control could be attained when all of the subsystems are non igasim moreover when the discrete time sss is composed of some igasim and non igasim subsystems a reverse mdadt based relationship between these two kinds of subsystems is established such that an igasim discrete time sss with incremental l2 gain property is obtained two numerical examples are presented to demonstrate the effectiveness of the results

gradient estimates for some diffusion semigroups consider the semigroup p t of an elliptic diffusion we describe a simple stochastic method providing gradient estimates on p tf if n is a manifold endowed with a connection the method can also be applied to the associated nonlinear semigroup q t acting on n valued maps with a localization technique we deduce gradient estimates for real harmonic functions or n valued harmonic maps moreover the results are extended to a class of hypoelliptic diffusions

gradient estimates for harmonic functions on regular domains in riemannian manifolds abstract derivative formulae for heat semigroups are used to give gradient estimates for harmonic functions on regular domains in riemannian manifolds this probabilistic method provides an alternative to coupling techniques as introduced by cranston and allows us to improve some known estimates we discuss two slightly different ways to exploit derivative formulae where each one should be interesting by itself

the witten complex and the degenerate morse inequalities on utilise le complexe introduit par witten pour demontrer des inegalites de morse dans le cas ou les points critiques de la fonction de morse sont isoles et aussi les inegalites de morse degenerees de bott

finite time control of interconnected impulsive switched systems with time varying delay in this paper the problem of finite time control is addressed for a class of interconnected impulsive switched systems with time varying delay and dynamical disturbance the concepts of finite time stability and finite time boundedness are extended to interconnected impulsive switched systems respectively by resorting to the average dwell time approach and constructing an appropriate lyapunov krasovskii functional some sufficient criteria are provided to cope with the problems of finite time boundedness and finite time stability then based on the decentralized switched control scheme a state feedback controller is established to ensure the finite time stability and the finite time boundedness finally an example and simulation result are demonstrated to illustrate the correctness and effectiveness of the proposed method

application of coupling methods to the neumann eigenvalue problem by using coupling methods some lower bounds are obtained for the first neumann eigenvalue on riemannian manifolds this method is new and the results improve some known estimates an example shows that our estimates can be sharp

methods for the analysis of sampled cohort data in the cox proportional hazards model methods are provided for regression parameter and cumulative baseline hazard estimation in the cox proportional hazards model when the cohort is sampled according to a predictable sampling probability law the key to the methodology is to define counting processes which count joint failure and sampled risk sets occurrences and to derive the appropriate intensities for these counting processes leading to estimation methods parallel to those for full cohort data these techniques are illustrated for a number of sampling designs including three novel designs counter matching with additional randomly sampled controls quota sampling of controls and nested case control sampling with number of controls dependent on the failure s exposure status general asymptotic theory is developed for the maximum partial likelihood estimator and cumulative baseline hazard estimator and is used to derive the asymptotic distributions for estimators from a large class of designs

a condition for the equivalence of coupling and shift coupling it is proved in this paper that a weak parabolic harnack inequality for a markov semigroup implies the existence of a coupling and a shift coupling for the corresponding process with equal chances of success this implies equality of the tail and invariant sigma fields for the diffusion as well as equality of the class of bounded parabolic functions and the class of bounded harmonic functions

score tests for homogeneity of regression effect in the proportional hazards model a simple model containing the proportional hazards regression model as a special case is presented the purpose of the model is to provide a framework in which specific alternatives to the proportional hazards assumption may be tested rank invariant score tests for linear quadratic or exponential trends can for instance all be undertaken within this framework in the case of the two sample problem the required calculations are shown to take a particularly simple form special consideration is given to the two sample case in which there is an inversion of the regression effect i e where the hazard functions cross at some given point both of the motivating examples are concerned with this problem computational aspects are relatively straightforward and some discussion on this is provided

finite time stability and stabilization of ito stochastic systems with markovian switching mode dependent parameter approach this technical note is concerned about the finite time stability and stabilization for ito stochastic systems with markovian switching a mode dependent parameter approach is proposed to give a sufficient condition for finite time stability and its superiority to common parameter approach is analyzed moreover the finite time stabilization is studied and two new sufficient conditions for the existence of state and output feedback controllers are presented in terms of coupled matrix inequalities a n mode algorithm is given for solving the obtained matrix inequalities arising from finite time stability stabilization finally an example is employed to illustrate the effectiveness of our obtained results

covariance and survivor function estimation using censored multivariate failure time data summary the covariance between counting process martingales is used to characterize the dependence between two failure time variates a representation of the bivariate survivor function is obtained in terms of the marginal survivor functions and this covariance function a closely related representation expresses the bivariate survivor function in terms of marginal survivor functions and a conditional covariance function leading to a new nonparametric survivor function estimator generalizations to higher dimensional failure time variates are also given simulation evaluations of the survivor function estimator are presented and generalizations to regression problems are outlined

correcting for selective compliance in a re employment bonus experiment we propose a two stage instrumental variable estimator that is consistent if there is selective compliance in the treatment group of a randomized experiment and the outcome variable is a censored duration the estimator assumes full compliance in the control group we use the estimator to re analyze data from the illinois re employment bonus experiment

fuzzy resilient energy to peak filtering for continuous time nonlinear systems the problem of resilient energy to peak filtering for a class of uncertain continuous time nonlinear systems is investigated in this paper a takagi sugeno fuzzy model with norm bounded uncertainties is used to represent the nonlinear plant attention is focused on the design of an energy to peak filter such that the filtering error system is asymptotically stable and the prescribed energy to peak filtering performance is guaranteed where the designed filter is assumed to have additive gain variations the proposed design is aimed at all filter matrices with gain variations which improves the existing results on resilient energy to peak filtering for continuous time systems a simulation example is provided to show the effectiveness of the proposed methods

new stability and stabilization results for discrete time switched systems abstract in this paper the stability and stabilization problems are considered for discrete time switched systems in both nonlinear and linear contexts by introducing the concept of forward average dwell time and using multiple sample lyapunov like functions variation the extended stability results for discrete time switched systems in the nonlinear setting are first derived then the criteria for stability and stabilization of linear switched systems are obtained via a newly constructed switching strategy which allows us to obtain exponential stability results a new kind of mode dependent controller is designed to realize the stability conditions expressed by the multiple sample lyapunov like functions variation based on this controller the stabilization conditions can be given in terms of linear matrix inequalities lmis which are easy to be checked by using recently developed algorithms in solving lmis finally a numerical example is provided to show the validity and potential of the results

the asymptotic joint distribution of the efficient scores test for the proportional hazards model calculated over time the log rank test derived by mantel 1966 and peto 1972 is a special case of the efficient scores test for the proportional hazards model it is used frequently in the analysis of survival duration in clinical trials where patients entering the study are assigned at random to different treatments and are followed until they either fail or the study is terminated most survival studies are designed so that the log rank test is calculated at the termination of the study however it is not unusual for the investigator to monitor the experiment periodically it would be very useful to know the joint distribution of the test statistic at different points in time in order to adjust for the effect of repeated significance testing although the joint distribution has not been derived previously sequential procedures for the analysis of survival data in clinical trials have been given by several authors including armitage 1975 pp 140 6 jones whitehead 1979 and nagelkerke hart 1980 in this paper we establish the asymptotic joint distribution of the more general efficient scores test for the proportional hazards model calculated at different points in time and indicate how this can be used for repeated significance testing

competing risks regression for stratified data for competing risks data the fine gray proportional hazards model for subdistribution has gained popularity for its convenience in directly assessing the effect of covariates on the cumulative incidence function however in many important applications proportional hazards may not be satisfied including multicenter clinical trials where the baseline subdistribution hazards may not be common due to varying patient populations in this article we consider a stratified competing risks regression to allow the baseline hazard to vary across levels of the stratification covariate according to the relative size of the number of strata and strata sizes two stratification regimes are considered using partial likelihood and weighting techniques we obtain consistent estimators of regression parameters the corresponding asymptotic properties and resulting inferences are provided for the two regimes separately data from a breast cancer clinical trial and from a bone marrow transplantation registry illustrate the potential utility of the stratified fine gray model

quasi maximum likelihood estimation of volatility with high frequency data this paper investigates the properties of the well known maximum likelihood estimator in the presence of stochastic volatility and market microstructure noise by extending the classic asymptotic results of quasi maximum likelihood estimation when trying to estimate the integrated volatility and the variance of noise this parametric approach remains consistent efficient and robust as a quasi estimator under misspecified assumptions moreover it shares the model free feature with nonparametric alternatives for instance realized kernels while being advantageous over them in terms of finite sample performance in light of quadratic representation this estimator behaves like an iterative exponential realized kernel asymptotically comparisons with a variety of implementations of the tukey hanning2 kernel are provided using monte carlo simulations and an empirical study with the euro us dollar future illustrates its application in practice

duality on gradient estimates and wasserstein controls abstract we establish a duality between l p wasserstein control and l q gradient estimate in a general framework our result extends a known result for a heat flow on a riemannian manifold especially we can derive a wasserstein control of a heat flow directly from the corresponding gradient estimate of the heat semigroup without using any other notion of lower curvature bound by applying our result to a subelliptic heat flow on a lie group we obtain a coupling of heat distributions which carries a good control of their relative distance

transport inequalities gradient estimates entropy and ricci curvature we present various characterizations of uniform lower bounds for the ricci curvature of a smooth riemannian manifold m in terms of convexity properties of the entropy considered as a function on the space of probability measures on m as well as in terms of transportation inequalities for volume measures heat kernels and brownian motions and in terms of gradient estimates for the heat semigroup 2004 wiley periodicals inc

estimates of logarithmic sobolev constant an improvement of bakry emery criterion this paper is mainly devoted to estimate the logarithmic sobolev abbrev l s constant for diffusion operators on manifold or in rd in most cases we study the lower bounds but a generalization to a korzeniowski j funct anal 71 1987 363 370 theorem 1 for the upper bound is also presented theorem 1 5 based on a simple observation due to j d deuschel and d w stroock j funct anal 92 1990 30 48 of the comparison between the l s constants for different potentials the powerful bakry emery criterion for the l s inequality is improved considerably in the paper especially for the manifolds with non positive sectional curvatures theorem 1 3 1 in terms of our notation r inf x p r infx tx m x 1 ricc hessv x x where x p is the distance betweenxand an arbitrary fixed pointp m the improvement can be roughly stated as follows the condition infr 0 r 0 for which the criterion is available is now replaced by supr 0 r 0

on estimation of the logarithmic sobolev constant and gradient estimates of heat semigroups this paper presents some explicit lower bound estimates of logarithmic sobolev constant for diffusion processes on a compact riemannian manifold with negative ricci curvature let ric k for some k 0 and d d be respectively the dimension and the diameter of the manifold if the boundary of the manifold is either empty or convex then the logarithmic sobolev constant for brownian motion is not less than

instrumental variable estimation in semi parametric additive hazards models instrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available two stage least squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data the semi parametric additive hazard model which can include time independent and time dependent covariate effects is particularly suited for the two stage residual inclusion method since it allows direct estimation of time independent covariate effects without restricting the effect of the residual on the hazard in this article we prove asymptotic normality of two stage residual inclusion estimators of regression coefficients in a semi parametric additive hazard model with time independent and time dependent covariate effects we consider the cases of continuous and binary exposure estimation of the conditional survival function given observed covariates is discussed and a resampling scheme is proposed to obtain simultaneous confidence bands the new methods are compared to existing ones in a simulation study and are applied to a real data set the proposed methods perform favourably especially in cases with exposure dependent censoring

nonparametric analysis of randomized experiments with missing covariate and outcome data abstract analysis of randomized experiments with missing covariate and outcome data is problematic because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data this article shows how population parameters can be bounded without making untestable distributional assumptions bounds are also derived under the assumption that covariate data are missing completely at random in each case the bounds are sharp they exhaust all of the information available given the data and the maintained assumptions the bounds are illustrated with applications to data obtained from a clinical trial and data relating family structure to the probability that a youth graduates from high school

estimating regression parameters using linear rank tests for censored data a class of estimates for regression parameters in a linear model with right censored data is proposed these estimates are derived by using linear rank tests for right censored data as estimating equations they are shown to be consistent and asymptotically normal with covariance matrix for which estimates are proposed efficient estimates within this class are derived together with conditions when they are fully efficient 0 introduction the problem of estimating regression coefficients using ranks has received much attention in the literature for uncensored data and the results are summarized very nicely in hettmansperger 1984 there has also been some work with censored data for the two sample problem specifically louis 1981 considered estimation of a scale change between two distributions with the use of a log rank test whereas wei and gail 1983 generalize to other linear rank tests as well we shall consider the general problem of multiple linear regression and show how linear rank tests can be used as estimating equations the resulting linear rank estimates will be shown to be asymptotically normal with variances that can be estimated consistently we shall also look at the efficiency of these estimates under various situations

integration by parts formula and shift harnack inequality for stochastic equations a new coupling argument is introduced to establish driver s integration by parts formula and shift harnack inequality unlike known coupling methods where two marginal processes with different starting points are constructed to move together as soon as possible for the new type coupling the two marginal processes start from the same point but their difference is aimed to reach a fixed quantity at a given time besides the integration by parts formula the new coupling method is also efficient to imply the shift harnack inequality differently from known harnack inequalities where the values of a reference function at different points are compared in the shift harnack inequality the reference function rather than the initial point is shifted a number of applications of the integration by parts and shift harnack inequality are presented the general results are illustrated by some concrete models including the stochastic hamiltonian system where the associated diffusion process can be highly degenerate delayed sdes and semi linear spdes

bounds on treatment effects on transitions abstract this paper considers the identification of treatment effects on conditional transition probabilities we show that even under random assignment only the instantaneous average treatment effect is point identified since treated and control units drop out at different rates randomization only ensures the comparability of treatment and controls at the time of randomization so that long run average treatment effects are not point identified instead we derive bounds on these average effects our bounds do not impose semi parametric restrictions for example proportional hazards we also explore assumptions such as monotone treatment response common shocks and positively correlated outcomes that tighten the bounds

second fundamental form and gradient of neumann semigroups abstract the second fundamental form for the boundary of a compact riemannian manifold is described by a short time behavior for the gradient of neumann semigroups as an application we prove that the manifold is convex if and only if the neumann heat semigroup p t satisfies the gradient estimate p t f e k t p t f for some constant k r and all t 0 f c 1

the radial part of brownian motion on a manifold a semimartingale property on montre que la discrepance entre les deux membres de la formule de ito forme un processus aleatoire monotone

functional inequalities for empty essential spectrum abstract in terms of the equivalence of poincare inequality and the existence of spectral gap the super poincare inequality is suggested in the paper for the study of essential spectrum it is proved for symmetric diffusions that such an inequality is equivalent to empty essential spectrum of the corresponding diffusion operator this inequality recovers known sobolev and nash type ones it is also equivalent to an isoperimetric inequality provided the curvature of the operator is bounded from below some results are also proved for a more general setting including symmetric jump processes moreover estimates of inequality constants are also presented which lead to a proof of a result on ultracontractivity suggested recently by d stroock finally concentration of reference measures for super poincare inequalities is studied the resulting estimates extend previous ones for poincare and log sobolev inequalities

heat kernel analysis on infinite dimensional heisenberg groups abstract we introduce a class of non commutative heisenberg like infinite dimensional lie groups based on an abstract wiener space the ricci curvature tensor for these groups is computed and shown to be bounded brownian motion and the corresponding heat kernel measures t t 0 are also studied we show that these heat kernel measures admit 1 gaussian like upper bounds 2 cameron martin type quasi invariance results 3 good l p bounds on the corresponding radon nikodym derivatives 4 integration by parts formulas and 5 logarithmic sobolev inequalities the last three results heavily rely on the boundedness of the ricci tensor

synchronization of complex dynamical networks by the incremental iss approach abstract we apply the theory of incremental input to state stability to the problem of synchronization in a complex dynamical network of identical nodes using chaotic nodes as a typical platform the proposed technique can achieve chaos synchronization using only partial state output information in the sense that it does not require all nodes to have access to the state output information from the seed node that produces the reference orbit for network synchronization examples and simulations demonstrate the applicability and effectiveness of the new synchronization method

incremental h control for switched nonlinear systems abstract in this paper we investigate the incremental h control for switched nonlinear systems by using a state dependent switching law and an average dwell time approach incorporated with multiple lyapunov functions even if all subsystems are unstable a sufficient condition for the incremental h control problem to be solvable is derived based on the design state dependent switching law furthermore when all subsystems are incrementally globally asymptotically stable igas the switched nonlinear system under the average dwell time scheme is igas and possesses a weighted incremental l2 gain then we extend this result to the case where both igas subsystems and unstable subsystems coexist if the activation time ratio between igas subsystems and unstable ones is not less than a specified constant sufficient conditions for the weighted incremental h performance of the switched system are guaranteed two numerical examples are given to illustrate the validity of the proposed approach

gradient estimates of dirichlet heat semigroups and application to isoperimetric inequalities by using probabilistic approaches some uniform gradient estimates are obtained for dirichlet heat semigroups on a riemannian manifold with boundary as an application lower bound estimates of isoperimetric constants are presented in terms of functional inequalities