The Gamma-frailty proportional hazards (PH) model is commonly used to analyze correlated survival data. Despite this model’s popularity, the analysis of correlated current status data under the Gamma-frailty PH model can prove to be challenging using traditional techniques. Consequently, in...

In this article, a class of transformed hazards models is proposed for recurrent gap time data, including both the proportional and additive hazards models as special cases. An estimating equation-based inference procedure is developed for the model parameters, and the asymptotic properties of...

Generalized Threshold Model (GTM) is a non-linear time series model which generalizes the Threshold Autoregressive Model (TAR) to implement the idea of the Generalized Linear Model under the threshold time series framework. However, the dispersion parameter is usually assumed as constant in the...

We consider here the problem of testing the effect of a subset of predictors for a regression model with predictor dimension fixed but ultra high dimensional responses. Because the response dimension is ultra high, the classical method of likelihood ratio test is no longer applicable. To solve...

Motivated from the stochastic representation of the univariate zero-inflated Poisson (ZIP) random variable, the authors propose a multivariate ZIP distribution, called as Type I multivariate ZIP distribution, to model correlated multivariate count data with extra zeros. The distributional theory...

A new methodological approach that enables the use of the maximum likelihood method in the Generalized Pareto Distribution is presented. Thus several models for the same data can be compared under Akaike and Bayesian information criteria. The view is based on a detailed theoretical study of the...

A systematic Bayesian framework is developed for physics constrained parameter inference of stochastic differential equations (SDE) from partial observations. Physical constraints are derived for stochastic climate models but are applicable for many fluid systems. A condition is derived for...

We consider an extension of Cook’s distance for generalized linear mixed models with the objective of identifying observations with high influence in the predicted conditional means of the response variable. The proposed distance can be decomposed into factors that help to distinguish between...

In the context of classification, it is a common phenomenon that high-dimensional data such as micro-array data consist of only a few informative components. If one uses standard statistical modeling and estimation procedures with entire information, it tends to overfit the data due to noise...

The bivariate random effects model has been advocated for the meta-analysis of diagnostic accuracy despite scarce information regarding its statistical performance for non-comparative categorical outcomes. Four staggered simulation experiments using a full-factorial design were conducted to...