Due to recent theoretical findings and advances in statistical computing, there has been a
rapid development of techniques and applications in the area of missing data analysis …

We consider the penalized generalized estimating equations (GEEs) for analyzing
longitudinal data with high-dimensional covariates, which often arise in microarray …

Variable selection plays an essential role in regression analysis as it identifies important
variables that are associated with outcomes and is known to improve predictive accuracy of …

We consider selecting both fixed and random effects in a general class of mixed effects
models using maximum penalized likelihood (MPL) estimation along with the smoothly …

In modern statistical applications, the dimension of covariates can be much larger than the
sample size. In the context of linear models, correlation screening (Fan & Lv, JR Statist. Soc …

We consider integrating a non-probability sample with a probability sample which provides
high dimensional representative covariate information of the target population. We propose …

Complementary features of randomized controlled trials (RCTs) and observational studies
(OSs) can be used jointly to estimate the average treatment effect of a target population. We …

In the analysis of observational studies, inverse probability weighting (IPW) is commonly
used to consistently estimate the average treatment effect (ATE) or the average treatment …

Penalized variable selection methods have been extensively studied for standard time-to-
event data. Such methods cannot be directly applied when subjects are at risk of multiple …

Psychological researchers often use standard linear regression to identify relevant
predictors of an outcome of interest, but challenges emerge with incomplete data and …