Gene set-based analysis methods have recently gained increasing popularity for analysis of microarray data. Several studies have indicated that the results from such methods are more reproducible and more easily interpretable than the results from single gene-based methods. A new method for...

Challenges in the analyses of growth mixture models include missing data, outliers, estimation, and model selection. Four non-ignorable missingness models to recover the information due to missing data, and three robust models to reduce the effect of non-normality are proposed. A full Bayesian...

This paper proposes the nonnegative-lasso method for variable selection in high dimensional sparse linear regression models with the nonnegative constraints on the coefficients. This method is an extension of Lasso and is shown to have variable selection consistency and estimation consistency...

Several Monte Carlo methods have been proposed for computing marginal likelihoods in Bayesian analyses. Some of these involve sampling from a sequence of intermediate distributions between the prior and posterior. A difficulty arises if the support in the posterior distribution is a proper...

This study introduces a new class of time series models capturing dynamic seasonality. Unlike traditional seasonal models that mainly focus on the mean process, our approach accommodates dynamic seasonality in the mean and variance processes. This feature allows us to statistically infer dynamic...

In promotion discrimination cases, individuals affected by discrimination may decide to retire earlier than otherwise. Two Cox proportional hazards models are used to describe the promotion process from non-retired employees and the retirement process, respectively. To account for a potential...

For the two-parameter exponential family, a linear Bayes method is proposed to simultaneously estimate the parameter vector consisting of location and scale parameters. The superiority of the proposed linear Bayes estimator (LBE) over the classical UMVUE is established in terms of the mean...

Penalized generalized estimating equations with Elastic Net or L2-Smoothly Clipped Absolute Deviation penalization are proposed to simultaneously select the most important variables and estimate their effects for longitudinal Gaussian data when multicollinearity is present. The method is able to...

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays more and more frequent and, unfortunately, classical model-based clustering techniques show a disappointing behavior in high-dimensional...

A state space mixed models for binary time series where the inverse link function is modeled to be a cumulative distribution function of the scale mixture of normal (SMN) distributions. Specific inverse links examined include the normal, Student-t, slash and the variance gamma links. The...