a markov chain model for animal estrous cycling data estrous cycling data contain sequences of characters e g dpemd each sequence represents an animal s estrous cycle with each character indicating the daily estrous cycle stage changes in the estrous cycle pattern which is determined by estrous stage lengths can provide information on adverse events stage lengths are not directly observable however interval censored lengths for all but the first and the last stages in a sequence can be extracted from the data we propose a markov chain model to approximate the estrous cycling process the transition probabilities from one stage to another can be derived by conditioning on stage lengths assuming weibull distribution for stage lengths with the second weibull parameter depending upon treatment effects and animal specific random effects regression models on censored stage lengths are fitted a bayesian approach is used for inference on dose effects the analysis is implemented with mcmc method in winbugs an estrous cycling data set from a national toxicology program study is analyzed as an example

weighted batch means for confidence intervals in steady state simulations we propose a new procedure for providing confidence interval estimators of the mean of a covariance stationary process the procedure a modification of the method of batch means is an improvement over existing methods when the process displays strong correlation and a comparatively small number of observations is available we assign weights to the observations within a batch the weights are determined by the order of the time series model fit to the process and by its estimated parameters for a given model and its parameters the weights minimize the variance of the weighted point estimator of the mean the point and variance estimators formed when these optimal weights are applied are unbiased the time series identification procedure and estimation of the parameters and weights bring in bias formulas for optimal weights are given for arp and ma1 processes experiments were conducted on ar1 ar2 and ma2 processes as well as on the m m 1 queue delay process and a periodic inventory system cost process results show that for processes with strong correlation over many lags as is typical in queue delay processes and inventory processes the achieved coverage of the constructed interval is closer to nominal levels than for the unweighted batch means method the coverage and average half length is not as greatly affected by the number of batches and nonnormality of the weighted batch means is not as severe

identification of the 1pl model with guessing parameter parametric and semi parametric results in this paper we study the identification of a particular case of the 3pl model namely when the discrimination parameters are all constant and equal to 1 we term this model 1pl g model the identification analysis is performed under three different specifications the first specification considers the abilities as unknown parameters it is proved that the item parameters and the abilities are identified if a difficulty parameter and a guessing parameter are fixed at zero the second specification assumes that the abilities are mutually independent and identically distributed according to a distribution known up to the scale parameter it is shown that the item parameters and the scale parameter are identified if a guessing parameter is fixed at zero the third specification corresponds to a semi parametric 1pl g model where the distribution g generating the abilities is a parameter of interest it is not only shown that after fixing a difficulty parameter and a guessing parameter at zero the item parameters are identified but also that under those restrictions the distribution g is not identified it is finally shown that after introducing two identification restrictions either on the distribution g or on the item parameters the distribution g and the item parameters are identified provided an infinite quantity of items is available

multiple comparison procedures for steady state simulations suppose that there are k 2 different systems i e stochastic processes where each system has an unknown steady state mean performance and unknown asymptotic variance we allow for the asymptotic variances to be unequal and for the distributions of the k systems to be different we consider the problem of running independent single stage simulations to make multiple comparisons of the steady state means of the different systems we derive asymptotically valid as the run lengths of the simulations of the systems tend to infinity simultaneous confidence intervals for each of the following problems all pairwise comparisons of means all contrasts multiple comparisons with a control and multiple comparisons with the best our confidence intervals are based on standardized time series methods and we establish the asymptotic validity of each under the sole assumption that the stochastic processes representing the simulation output of the different systems satisfy a functional central limit theorem although simulation is the context of this paper the results naturally apply to asymptotically stationary time series

integrals for monoidal hom hopf algebras and their applications integrals of monoidal hom hopf algebras are introduced and the existence and uniqueness of integrals for finite dimensional monoidal hom hopf algebras are investigated first then integrals are applied to the maschke type theorem for monoidal hom hopf algebras controlling the semisimplicity and separability of monoidal hom hopf algebras further monoidal hom algebras are characterized with additional frobenius property and the question when finite dimensional monoidal hom hopf algebras are frobenius is studied as applications of integrals the maschke type theorem for hom smash product is given and the morita context in the hom category h mk is constructed

batch size effects in the analysis of simulation output batching is a commonly used method for calculating confidence intervals on the mean of a sequence of correlated observations arising from a simulation experiment several recent papers have considered the effect of using batch sizes too small to satisfy assumptions of normality and or independence and the resulting incorrect probabilities of the confidence interval covering the mean this paper quantifies the effects of using batch sizes larger than necessary to satisfy normality and independence assumptions these effects include 1 correct probability of covering the mean 2 an increase in expected half length 3 an increase in the standard deviation and coefficient of variation of the half length and 4 an increase in the probability of covering points not equal to the mean for any sample size and independent and normal batch means the results are 1 the effects of less than 10 batches are large and the effects of more than 30 batches small and 2 additional batches have lesser effects on

minimax linear observers and regulators for stochastic systems with uncertain second order statistics the problem of minimax design of linear observers and regulators for linear time varying multivariable stochastic systems with uncertain models of their second order statistics is treated in this paper general classes of allowable covariance matrices and means of the process and observation noises and of the random initial condition are considered a game formulation of the problem is adopted and it is shown that the optimal filter for the least favorable set of covariances is minimax robust for each of the filtering situations analyzed conditions satisfied by the saddle point solutions are given and their utility for finding the worst case covariances is illustrated by way of several examples of uncertainty classes of practical interest

a bayesian approach to ordering gene markers a technique is presented whereby a marker map can be constructed using resource family data with an entire class of missing data the focus is on a half sib design where there is only information on a single parent and its progeny a bayesian approach is utilised with solutions obtained via a markov chain monte carlo algorithm features of the approach include the capacity to determine parameters for the ungenotyped dam population the ability to incorporate published information and its reliability and the production of posterior densities and the consequent deduction of a wide range of inferences these features are demonstrated through the analysis of simulated and experimental data

optimal mean squared error batch sizes when an estimator of the variance of the sample mean is parameterized by batch size one approach for selecting batch size is to pursue the minimal mean squared error mse we show that the convergence rate of the variance of the sample mean and the bias of estimators of the variance of the sample mean asymptotically depend on the data process only through its marginal variance and the sum of the autocorrelations weighted by their absolute lags combining these results with variance results of goldsman and meketon we obtain explicit asymptotic approximations for mse optimal batch size optimal mse and robustness for four quadratic form estimators of the variance of the sample mean our empirical results indicate that the asymptotic approximations are reasonably accurate for sample sizes seen in practice although we do not discuss batch size estimation procedures the empirical results suggest that the explicit asymptotic batch size approximation which depends only on a summary measure which we re

the hom yang baxter equation hom lie algebras and quasi triangular bialgebras we study a twisted version of the yang baxter equation called the hom yang baxter equation hybe which is motivated by hom lie algebras three classes of solutions of the hybe are constructed one from hom lie algebras and the others from drinfeld s dual quasi triangular bialgebras each solution of the hybe can be extended to operators that satisfy the braid relations assuming an invertibility condition these operators give a representation of the braid group

standardized time series l p norm variance estimators for simulations this paper studies a class of estimators for the variance parameter of a stationary stochastic process the estimators are based on lp norms of standardized time series and they generalize previously studied estimators due to schruben we show that the new estimators have some desirable properties they are asymptotically unbiased and have low asymptotic variance we also illustrate empirically the performance of the lp norm estimators on various stochastic processes

relative hom hopf modules and total integrals let h be a monoidal hom hopf algebra and a a right h hom comodule algebra we first investigate the criterion for the existence of a total integral of a in the setting of monoidal hom hopf algebras also we prove that there exists a total integral h a if and only if any representation of the pair h a is injective in a functorial way as a corepresentation of h which generalizes doi s result finally we define a total quantum integral h hom h a and prove the following affineness criterion if there exists a total quantum integral and the canonical map a ba a h a bb 1 a b 0 b 1 is surjective then the induction functor a b k b k ah is an equivalence of categories

markov chain monte carlo convergence diagnostics a comparative review abstract a critical issue for users of markov chain monte carlo mcmc methods in applications is how to determine when it is safe to stop sampling and use the samples to estimate characteristics of the distribution of interest research into methods of computing theoretical convergence bounds holds promise for the future but to date has yielded relatively little of practical use in applied work consequently most mcmc users address the convergence problem by applying diagnostic tools to the output produced by running their samplers after giving a brief overview of the area we provide an expository review of 13 convergence diagnostics describing the theoretical basis and practical implementation of each we then compare their performance in two simple models and conclude that all of the methods can fail to detect the sorts of convergence failure that they were designed to identify we thus recommend a combination of strategies aimed at evaluating and accelerating mcmc sampler convergence including ap

quantile estimation in dependent sequences standard nonparametric estimators of quantiles based on order statistics can be used not only when the data are i i d but also when the data are drawn from a stationary i mixing process of continuous random variables however when the random variables are highly positively correlated the sample sizes needed for estimating extreme quantiles become computationally unmanageable this paper gives a practical scheme based on a maximum transformation in a two way layout of the data that reduces the sample size sufficiently to allow an experimenter to obtain a point estimate of an extreme quantile the paper gives three schemes that lead to confidence interval estimates for the quantile one uses a spectral analysis of the reduced sample the other two averaged group quantiles and nested group quantiles are extensions of the method of batched means to quantile estimation these two schemes give even greater data compaction than the first scheme none of the schemes requires that the process being simulated is regenerative

estimating the steady state mean from short transient simulations steady state mean performance is a common basis for evaluating discrete event simulation models however analysis is complicated by autocorrelation and initial transients we present confidence interval procedures for estimating the steady state mean of a stochastic process from observed time series which may be short autocorrelated and transient we extend the generalized least squares estimator of snell and schruben iie trans 17 1985 354 and develop confidence interval procedures for single and multiple replication experiments the procedures are asymptotically valid and for short series with reasonable initializations e g empty and idle are comparable or superior to existing procedures further we demonstrate and explain the robustness of the weighted batch means procedure of bischak et al manage sci 39 1993 1002 to initialization bias the proposed confidence interval procedure requires neither truncation of initial observations nor choice of batch size and permits the existence of steady state mean to be inferred from the data

on hom algebras with surjective twisting abstract a hom associative structure is a set a together with a binary operation and a self map such that an twisted version of associativity is fulfilled in this paper we assume that is surjective we show that in this case under surprisingly weak additional conditions on the multiplication the binary operation is a twisted version of an associative operation as an application an earlier result fregier and gohr 1 on weakly unital hom algebras is recovered with a different proof in the second section consequences for the deformation theory of hom algebras with surjective twisting map are discussed

deformations of lie algebras using σ derivations in this article we develop an approach to deformations of the witt and virasoro algebras based on sigma derivations we show that sigma twisted jacobi type identity holds for generators of such deformations for the sigma twisted generalization of lie algebras modeled by this construction we develop a theory of central extensions we show that our approach can be used to construct new deformations of lie algebras and their central extensions which in particular include naturally the q deformations of the witt and virasoro algebras associated to q difference operators providing also corresponding q deformed jacobi identities c 2005 elsevier inc all rights reserved

on nonasymptotic optimal stopping criteria in monte carlo simulations we consider the setting of estimating the mean of a random variable by a sequential stopping rule monte carlo mc method the performance of a typical second moment based sequential stopping rule mc method is shown to be unreliable in such settings both by numerical examples and through analysis by analysis and approximations we construct a higher moment based stopping rule which is shown in numerical examples to perform more reliably and only slightly less efficiently than the second moment based stopping rule

symmetries and the u condition in hom yetter drinfeld categories let h s be a monoidal hom hopf algebra and hydhh the hom yetter drinfeld category over h then in this paper we first find sufficient and necessary conditions for hydhh to be symmetric and pseudosymmetric respectively second we study the u condition in hydhh and show that the hom yetter drinfeld module h adjoint resp h m coadjoint satisfies the u condition if and only if s2 id finally we prove that hydhh over a triangular resp cotriangular hom hopf algebra contains a rich symmetric subcategory

domain level covariance analysis for multilevel survey data with structured nonresponse health care quality surveys in the united states are administered to individual respondents i e hospital patients health plan members to evaluate the performance of health care organizations i e hospitals health plans which thus constitute estimation domains for better understanding and more parsimonious reporting of dimensions of quality we analyze relationships among quality measures at the domain level rather than specifying a full parametric model for the observed responses and the nonresponse patterns at the lower patient level we first fit generalized variance covariance functions that take into account nonresponse patterns in the survey responses then specify a likelihood function for the domain mean responses using these generalized variance covariance functions this allows us to model directly the relationships among domain means for different items because the response scales are bounded we assume that these means follow a truncated multivariate normal distribution we calcu

the kalman bucy filter accuracy in the guaranteed parameter estimation problem with uncertain statistics the guaranteed parameter estimation problem for a linear dynamical system with uncertain disturbance statistics is considered the estimate of the kalman bucy filter nonoptimality degree is constructed it is based on the duality theory this estimate is determined by the kalman bucy filter characteristics only and can be calculated without knowledge of the accurate solution of the optimal guaranteed estimation problem

a direct monte carlo approach for bayesian analysis of the seemingly unrelated regression model abstract computationally efficient methods for bayesian analysis of seemingly unrelated regression sur models are described and applied that involve the use of a direct monte carlo dmc approach to calculate bayesian estimation and prediction results using diffuse or informative priors this dmc approach is employed to compute bayesian marginal posterior densities moments intervals and other quantities using data simulated from known models and also using data from an empirical example involving firms sales the results obtained by the dmc approach are compared to those yielded by the use of a markov chain monte carlo mcmc approach it is concluded from these comparisons that the dmc approach is worthwhile and applicable to many sur and other problems

covariance structure of wavelet coefficients theory and models in a bayesian perspective we present theoretical results on the random wavelet coefficients covariance structure we use simple properties of the coefficients to derive a recursive way to compute the within and across scale covariances we point out a usetul link between the algorithm proposed and the two dimensional discrete wavelel transform we then focus on bayesian wavelet shrinkage for estimating a function from noisy data a prior distribution is imposed on the coefficients of the unknown function we show how our findings on the covariance structure make it possible to specify priors that take into account the full correlation between coefficients through a parsimonious number of hyperparameters we use markov chain monte carlo rnethods to estirnate the parameters and illustrate our method on bench mark simulated signals

confidence intervals for steady state simulations i a survey of fixed sample size procedures we consider the problem of constructing a confidence interval for the steady state mean of a stochastic process by means of simulation and study the five main methods which have been proposed replication batch means autoregressive representation spectrum analysis and regeneration cycles for the case when the length of the simulation is fixed in advance comparing the performances of these methods on stochastic models with known steady state means we find that the simulator should exercise caution in interpreting the results from a simulation of fixed length and that the length of a simulation adequate for acceptable performance is highly model dependent we also investigate possible sources of error for the methods and conclude that variance estimator bias is more important than point estimator bias in confidence interval coverage degradation

the kalman bucy filter in the guaranteed estimation problem the optimal guaranteed a priori estimation problem is considered the kalman bucy filter is used for the approximate solving of this problem the analytical estimate for the nonoptimality degree of the kalman bucy filter is obtained this estimate is determined by the kalman bucy filter characteristics only thus the kalman bucy filter efficiency can be established without accurate solving of the difficult optimal guaranteed estimation problem

simulation output analysis using standardized time series the method of standardized time series sts was proposed by schruben as an approach for constructing asymptotic confidence intervals for the steady state mean from a single simulation run the sts method cancels out the variance constant while other methods attempt to consistently estimate the variance constant our goal in this paper is to generalize the sts method and to study some of its basic properties starting from a functional central limit theorem fclt for the sample mean of the simulated process a class of mappings of c 0 1 to is identified each of which leads to a sts confidence interval one of these mappings leads to the batch means method a lower bound is obtained for the expected length of the asymptotic as the run size becomes large sts confidence intervals this lower bound is not attained but can be approached arbitrarily closely by sts confidence intervals methods that consistently estimate the variance constant do realize this lower bound the variance of the length of a sts confidence interval is of larger order in the run length than is that for the regenerative method

simulation run length control in the presence of an initial transient this paper studies the estimation of the steady state mean of an output sequence from a discrete event simulation it considers the problem of the automatic generation of a confidence interval of prespecified width when there is an initial transient present it explores a procedure based on schruben s brownian bridge model for the detection of nonstationarity and a spectral method for estimating the variance of the sample mean the procedure is evaluated empirically for a variety of output sequences the performance measures considered are bias confidence interval coverage mean confidence interval width mean run length and mean amount of deleted data if the output sequence contains a strong transient then inclusion of a test for stationarity in the run length control procedure results in point estimates with lower bias narrower confidence intervals and shorter run lengths than when no check for stationarity is performed if the output sequence contains no initial transient then the performance measures of the procedure with a stationarity test are only slightly degraded from those of the procedure without such a test if the run length is short relative to the extent of the initial transient the stationarity tests may not be powerful enough to detect the transient resulting in a procedure with unreliable point and interval estimates

optimal tests for initialization bias in simulation output we present a family of tests for detecting initialization bias in the mean of a simulation output series using a hypothesis testing framework the null hypothesis is that the output mean does not change throughout the simulation run the alternative hypothesis specifies a general transient mean function the tests are asymptotically optimal based on cumulative sums of deviations about the sample mean a particular test in this family is applied to a variety of simulation models the test requires very modest computation and appears to be both robust and powerful

a semiparametric stochastic volatility model abstract in this paper the correlation structure in the classical leverage stochastic volatility sv model is generalized based on a linear spline in the new model the correlation between the return and volatility innovations is time varying and depends nonparametrically on the type of news arrived to the market theoretical properties of the proposed model are examined the model estimation and comparison are conducted by bayesian methods the performance of the estimates are examined in simulations the new model is fitted to daily and weekly us data and compared with the classical sv and garch models in terms of their in sample and out of sample performances empirical results suggest evidence in favor of the proposed model in particular the new model finds strong evidence of time varying leverage effect in individual stocks when the classical model fails to identify the leverage effect

an investigation of finite sample behavior of confidence interval estimators we investigate the small sample behavior and convergence properties of confidence interval estimators cies for the mean of a stationary discrete process we consider cies arising from nonoverlapping batch means overlapping batch means and standardized time series all of which are commonly used in discrete event simulation the performance measures of interest are the coverage probability and the expected value and variance of the half length we use empirical and analytical methods to make detailed comparisons regarding the behavior of the cies for a variety of stochastic processes all the cies under study are asymptotically valid however they are usually invalid for small sample sizes we find that for small samples the bias of the variance parameter estimator figures significantly in cie coverage performance the less bias the better a secondary role is played by the marginal distribution of the stationary process we also point out that some cies require fewer observations before manifesting

a markov chain monte carlo scheme for parameter estimation and inference in conceptual rainfall runoff modeling a fully bayesian approach to parameter estimation and inference in conceptual rainfall runoff models crrms is presented computations are performed using a markov chain monte carlo mcmc method based on the metropolis hastings algorithm single site and block updating schemes are used for model parameters subject to nonnegativity restrictions as well as interval equality and order constraints diagnostics for the convergence of the markov chain and crrm assessment are also considered the mcmc approach produces samples from the joint posterior distribution of the model parameters this provides more information than single point estimates and avoids the need to use a normal approximation to the posterior as the basis for inference the methodology is demonstrated using an eight parameter conceptual rainfall runoff model and two case studies from southeastern australia the first case study considers a watershed with high runoff yield over a 12 year period the second case study considers a watershed with low yield over a 17 year period the results indicate that 1 bayesian methods provide an objective framework for model criticism and choice 2 the proposed strategies for handling constraints on model parameters are effective 3 the model parameters are sensitive to likelihood function selection 4 the conventional approach of using a power transformation and an autoregressive process to stabilize error variance and model dependence in the residuals may have limited success and 5 some care is required in the implementation of the mcmc approach and reliable results will be difficult to obtain when crrm complexity exceeds the limitations of the rainfall runoff data at hand a key finding is that the mcmc scheme presented herein provides a powerful means of identifying specific inadequacies in the structure of crrms

a bayesian chi squared test for hypothesis testing abstract a new bayesian test statistic is proposed to test a point null hypothesis based on a quadratic loss the proposed test statistic may be regarded as the bayesian version of the lagrange multiplier test its asymptotic distribution is obtained based on a set of regular conditions and follows a chi squared distribution when the null hypothesis is correct the new statistic has several important advantages that make it appealing in practical applications first it is well defined under improper prior distributions second it avoids jeffrey lindley s paradox third it always takes a non negative value and is relatively easy to compute even for models with latent variables fourth its numerical standard error is relatively easy to obtain finally it is asymptotically pivotal and its threshold values can be obtained from the chi squared distribution the method is illustrated using some real examples in economics and finance

smooth estimation of survival functions and hazard ratios from interval censored data using bayesian penalized b splines we discuss the use of bayesian p spline and of the composite link model to estimate survival functions and hazard ratios from interval censored data if one further assumes proportionality of the hazards the proposed strategy provides a smoothed estimate of the baseline hazard along with estimates of global covariate effects the frequentist properties of our bayesian estimators are assessed by an extensive simulation study we further illustrate the methodology by three examples showing that the proportionality of the hazards might also be found inappropriate from interval censored data

estimation of infection and recovery rates for highly polymorphic parasites when detectability is imperfect using hidden markov models a bayesian hierarchical model is proposed for estimating parasitic infection dynamics for highly polymorphic parasites when detectability of the parasite using standard tests is imperfect the parasite dynamics are modelled as a non homogeneous hidden two state markov process where the observed process is the detection or failure to detect a parasitic genotype this is assumed to be conditionally independent given the hidden process that is the underlying true presence of the parasite which evolves according to a first order markov chain the model allows the transition probabilities of the hidden states as well as the detectability parameter of the test to depend on a number of covariates full bayesian inference is implemented using markov chain monte carlo simulation the model is applied to a panel data set of malaria genotype data from a randomized controlled trial of bed nets in tanzanian children aged 6 30 months with the age of the host and bed net use as covariates this analysis confirmed that the duration of infections with parasites belonging to the msp 2 fc27 allelic family increased with age

two stage stopping procedures based on standardized time series we propose some new two stage stopping procedures to construct absolute width and relative width confidence intervals for a simulation estimator of the steady state mean of a stochastic process the procedures are based on the method of standardized time series proposed by schruben and on stein s two stage sampling scheme we prove that our two stage procedures give rise to asymptotically valid confidence intervals as the prescribed length of the confidence interval approaches zero and the size of the first stage grows to infinity the sole assumption required is that the stochastic process satisfy a functional central limit theorem

confidence interval estimation using standardized time series observations of a stationary stochastic process can be transformed into a standardized time series this paper presents a lemma giving the asymptotic properties of this standardized series under quite general conditions in particular the conditions are satisfied by stationary discrete event simulations confidence intervals can be constructed using this lemma for illustration we develop two easily computed interval estimators for the process mean when independent replications of the series are available such as in computer simulation experiments these interval estimators may be combined with the classical confidence interval estimator these interval estimators also tend to compensate for simulation initialization bias if the sign of the bias is known in an empirical study using three elementary simulated processes the interval estimators presented here compare favorably with the classical interval estimator in a recent paper goldsman and schruben goldsman d l schruben 1982 asymptotic properties of some confidence interval estimators tech rep 544 school of o r i e cornell university ithaca ny show that the asymptotic properties of the confidence intervals presented in this paper strictly dominate those of classical confidence intervals

multiple model evaluation absent the gold standard through model combination we describe a method for evaluating an ensemble of predictive models given a sample of observations comprising the model predictions and the outcome event measured with error our formulation allows us to simultaneously estimate measurement error parameters true outcome the gold standard and a relative weighting of the predictive scores we describe conditions necessary to estimate the gold standard and to calibrate these estimates and detail how our approach is related to but distinct from standard model combination techniques we apply our approach to data from a study to evaluate a collection of brca1 brca2 gene mutation prediction scores in this example genotype is measured with error by one or more genetic assays we estimate true genotype for each individual in the data set operating characteristics of the commonly used genotyping procedures and a relative weighting of the scores finally we compare the scores against the gold standard genotype and find that mendelian scores are on averag

an analytical evaluation of alternative strategies in steady state simulation we consider the simple technique of making independent and probabilistically identical simulation replications to estimate the steady state mean of a stochastic process for a fixed total sample size using a first order autoregressive model with high autocorrelation and time dependent distributions we analytically quantify and investigate the effects of the number of replications and of initial deletions of output on several different measures of point estimator and confidence interval quality this analysis leads to general recommendations on the choice of replications and deletion in particular we conclude that deletion of some amount of the initial output in a replication can be an effective and efficient method of dealing with initialization bias this conclusion differs from several previous studies on the efficacy of deletion

bayesian analysis and classification of two enzyme linked immunosorbent assay tests without a gold standard reconciling two quantitative elisa tests for an antibody to an rna virus in a situation without a gold standard and where false negatives may occur is the motivation for this work false negatives occur when access of the antibody to the binding site is blocked based on the mechanism of the assay a mixture of four bivariate normal distributions is proposed with the mixture probabilities depending on a two stage latent variable model including the prevalence of the antibody in the population and the probabilities of blocking on each test there is prior information on the prevalence of the antibody and also on the probability of false negatives and so a bayesian analysis is used the dependence between the two tests is modeled to be consistent with the biological mechanism bayesian decision theory is utilized for classification the proposed method is applied to the motivating data set to classify the data into two groups those with and those without the antibody simulation studies describe the properties of the estimation and the classification sensitivity to the choice of the prior distribution is also addressed by simulation the same model with two levels of latent variables is applicable in other testing procedures such as quantitative polymerase chain reaction tests where false negatives occur when there is a mutation in the primer sequence

on the asymptotic validity of fully sequential selection procedures for steady state simulation we present fully sequential procedures for steady state simulation that are designed to select the best of a finite number of simulated systems when best is defined by the largest or smallest long run average performance we also provide a framework for establishing the asymptotic validity of such procedures and prove the validity of our procedures an example based on the m m 1 queue is given

bayesian lasso for semiparametric structural equation models there has been great interest in developing nonlinear structural equation models and associated statistical inference procedures including estimation and model selection methods in this paper a general semiparametric structural equation model ssem is developed in which the structural equation is composed of nonparametric functions of exogenous latent variables and fixed covariates on a set of latent endogenous variables a basis representation is used to approximate these nonparametric functions in the structural equation and the bayesian lasso method coupled with a markov chain monte carlo mcmc algorithm is used for simultaneous estimation and model selection the proposed method is illustrated using a simulation study and data from the affective dynamics and individual differences adid study results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model

the category of yetter drinfel d hom modules and the quantum hom yang baxter equation in this paper we introduce the category of yetter drinfel d hom modules which is a braided monoidal category and show that the category of yetter drinfel d hom modules is a full monoidal subcategory of the left center of left hom module category also we study the equivalent relationship between the category of yetter drinfel d hom modules and the category of hom modules over the drinfel d double finally the faddeev reshetikhin takhtajan frt type theorem for the quantum hom yang baxter equation is investigated

on leverage in a stochastic volatility model this paper is concerned with the specification for modelling financial leverage effect in the context of stochastic volatility sv models two alternative specifications co exist in the literature one is the euler approximation to the well known continuous time sv model with leverage effect and the other is the discrete time sv model of jacquier et al j econometrics 122 2004 185 using a gaussian nonlinear state space form with uncorrelated measurement and transition errors i show that it is easy to interpret the leverage effect in the conventional model whereas it is not clear how to obtain and interpret the leverage effect in the model of jacquier et al empirical comparisons of these two models via bayesian markov chain monte carlo mcmc methods further reveal that the specification of jacquier et al is inferior simulation experiments are conducted to study the sampling properties of bayes mcmc for the conventional model

bayesian clustering and product partition models summary we present a decision theoretic formulation of product partition models ppms that allows a formal treatment of different decision problems such as estimation or hypothesis testing and clustering methods simultaneously a key observation in our construction is the fact that ppms can be formulated in the context of model selection the underlying partition structure in these models is closely related to that arising in connection with dirichlet processes this allows a straightforward adaptation of some computational strategies originally devised for nonparametric bayesian problems to our framework the resulting algorithms are more flexible than other competing alternatives that are used for problems involving ppms we propose an algorithm that yields bayes estimates of the quantities of interest and the groups of experimental units we explore the application of our methods to the detection of outliers in normal and student t regression models with clustering structure equivalent to that induced by a dirichlet process prior we also discuss the sensitivity of the results considering different prior distributions for the partitions

a wavelet based spectral procedure for steady state simulation analysis abstract we develop wassp a wavelet based spectral method for steady state simulation analysis first wassp determines a batch size and a warm up period beyond which the computed batch means form an approximately stationary gaussian process next wassp computes the discrete wavelet transform of the bias corrected log smoothed periodogram of the batch means using a soft thresholding scheme to denoise the estimated wavelet coefficients then taking the inverse discrete wavelet transform of the thresholded wavelet coefficients wassp computes estimators of the batch means log spectrum and the steady state variance parameter i e the sum of covariances at all lags for the original unbatched process finally by combining the latter estimator with the batch means grand average wassp provides a sequential procedure for constructing a confidence interval on the steady state mean that satisfies user specified requirements concerning absolute or relative precision as well as coverage probability an experimental performance evaluation demonstrates wassp s effectiveness compared with other simulation analysis methods

cramer von mises variance estimators for simulations we study estimators for the variance parameter 2 of a stationary process the estimators are based on weighted cramer von mises statistics and certain weightings yield estimators that are first order unbiased for 2 we derive an expression for the asymptotic variance of the new estimators this expression is then used to obtain the first order unbiased estimator having the smallest variance among fixed degree polynomial weighting functions our work is based on asymptotic theory however we present exact and empirical examples to demonstrate the new estimators small sample robustness we use a single batch of observations to derive the estimators asymptotic properties and then we compare the new estimators among one another in real life applications one would use more than one batch we indicate how this generalization can be carried out

asymptotic properties of some confidence interval estimators for simulation output the classical confidence interval estimator commonly used in simulation is compared with four new estimators based on standardization of a time series presented in a previous paper these new interval estimators are shown to have asymptotic properties that strictly dominate the classical estimator when used with data from independent simulation replications or means of batched observations from a single simulation run two of the new estimators also can be used with a single unbatched replication of a simulation program a situation where the classical estimator is not defined

integrals quantum galois extensions and the affineness criterion for quantum yetter drinfel d modules abstract in this paper we shall generalize the notion of an integral on a hopf algebra introduced by sweedler by defining the more general concept of an integral of a threetuple h a c where h is a hopf algebra coacting on an algebra a and acting on a coalgebra c we prove that there exists a total integral c hom c a of h a c if and only if any representation of h a c is injective in a functorial way as a corepresentation of c in particular the quantum integrals associated to yetter drinfel d modules are defined let now a be an h bicomodule algebra h y d a the category of quantum yetter drinfel d modules and b a a s 1 a 1 a 1 a 0 1h a the subalgebra of coinvariants of the verma structure a h y d a we shall prove the following affineness criterion if there exists h hom h a a total quantum integral and the canonical map a b a h a a b b s 1 b 1 b 1 ab 0 is surjective i e a b is a quantum homogeneous space then the induction functor b a m b h y d a is an equivalence of categories the affineness criteria proven by cline parshall and scott and independently by oberst for affine algebraic groups schemes and schneider in the noncommutative case are recovered as special cases

interval coverage in multiclass queues using batch mean estimates we investigate the fixed sample size batch mean procedure for creating confidence intervals from simulated data obtained from a stochastic queueing system with multiple customer classes we show that for a multiclass m m 1 queue serial correlation between customers of the same class decreases to zero as the number of customer classes increases we also derive a closed form expression for the asymptotic variance of waiting time by customer type we then empirically examine batch mean estimator coverage for a simple queue with multiple customer classes we find that batch mean estimators perform better in terms of coverage and interval half width in multiclass queues with a fixed number of observations per class than in the traditionally studied single class systems we also examine the effect of multiple classes where the total computational effort remains fixed