In a Poisson regression model in which observations are either clustered or represented by repeated measurements of counts, the number of observed zero counts is sometimes greater than the expected frequency by the Poisson distribution and overdispersion may remain even after modeling excess...

We develop two conditional tests for homogeneity of zero-inflated Poisson (ZIP) and Poisson-hurdle distributions. A Monte Carlo method is proposed for approximating the reference distributions of these tests. The techniques are applied to two examples.

The frailty model is one of the most popular models used to analyze clustered failure time data, where the frailty term is used to assess an association within each cluster. The frailty model based on the semiparametric accelerated failure time model attracts less attention than the one based on...

It is often the case that an outcome of interest is observed for a restricted non-randomly selected sample of the population. In such a situation, standard statistical analysis yields biased results. This issue can be addressed using sample selection models which are based on the estimation of...

Constructing generative models for functional observations is an important task in statistical functional analysis. In general, functional data contains both phase (or x or horizontal) and amplitude (or y or vertical) variability. Traditional methods often ignore the phase variability and focus...

This paper deals with the Bayesian inference of the linear hazard rate (LHR) distribution under a progressively censoring scheme. A unified treatment of both Type I and Type II censoring is presented under independent gamma priors for the parameters, that yields the posteriors as mixtures of...

We consider the problem of variable selection for the generalized linear models (GLMs) with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold generalized estimating equations (SGEE). The proposed procedure automatically eliminates inactive...

Several bandwidth selection procedures for kernel density estimation of a random variable that is sampled under random double truncation are introduced and compared. The motivation is based on the fact that this type of incomplete data is often encountered in astronomy and medicine. The...

Fiducial quantities are proposed to construct approximate tolerance limits and intervals for functions of some discrete random variables. Using established fiducial quantities for binomial proportions, Poisson rates, and negative binomial proportions, an approach is demonstrated to handle...

With three ordinal diagnostic categories, the most commonly used measure for the overall diagnostic accuracy is the volume under the ROC surface (VUS), which is the extension of the area under the ROC curve (AUC) for binary diagnostic outcomes. This article proposes two kernel smoothing based...