Mixture of t factor analyzers (MtFA) have been shown to be a sound model-based tool for robust clustering of high-dimensional data. This approach, which is deemed to be one of natural parametric extensions with respect to normal-theory models, allows for accommodation of potential noise...

The paper proposes Bayesian framework in an M/G/1 queuing system with optional second service. The semi-parametric model based on a finite mixture of Gamma distributions is considered to approximate both the general service and re-service times densities in this queuing system. A Bayesian...

Variable selection and estimation in proportional hazards models with additive relative risk is considered. Both objectives are achieved using a penalized partial likelihood with a group nonconcave penalty. Oracle properties of the estimator are demonstrated, when the dimensionality is allowed...

This paper proposes a new method for choosing regression models which may produce multiple models with sufficient explanatory power and parsimony unlike the traditional model selection procedures that aim at obtaining a single best model. The method ensures interpretability of the resulting...

The burgeoning growth of health care spending has become a major concern to policy makers, making the modeling of health care expenditure valuable in their decision-making processes. The challenges of health care expenditure analysis are two-fold: the exceptional skewness of its distribution as...

An important problem in high-dimensional data analysis is determining whether sample points are uniformly distributed (i.e., exhibit complete spatial randomness) over some compact support, or rather possess some underlying structure (e.g., clusters or other nonhomogeneities). We propose two new...

The identification of an appropriate multivariate copula for capturing the dependence structure in multivariate data is not straightforward. The reason is because standard multivariate copulas (such as the multivariate Gaussian, Student-t, and exchangeable Archimedean copulas) lack flexibility...

Semi-supervised classification can help to improve generative classifiers by taking into account the information provided by the unlabeled data points, especially when there are far more unlabeled data than labeled data. The aim is to select a generative classification model using both unlabeled...

Usually patients are allowed to switch their treatments in clinical trials out of an ethical consideration. This complicates the modeling of the trial, as well as the analyzing process of the collected data. For group-parallel clinical trials which contain two groups (one is the test and the...

When units belong to a specific group, such as employees nested within companies, the data present a hierarchical structure that can be modeled by using mixed models. In addition to fixed effects, these models estimate the random effects for each group. The problem of assigning values to the...