Varying coefficient regression models are known to be very useful tools for analysing the
relation between a response and a group of covariates. Their structure and interpretability …

Penalized regression is an attractive framework for variable selection problems. Often,
variables possess a grou** structure, and the relevant selection problem is that of …

The varying coefficient model is an important class of nonparametric statistical model, which
allows us to examine how the effects of covariates vary with exposure variables. When the …

Gene–environment interactions have important implications for elucidating the genetic basis
of complex diseases beyond the joint function of multiple genetic factors and their …

This article is concerned with feature screening and variable selection for varying coefficient
models with ultrahigh-dimensional covariates. We propose a new feature screening …

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

Partially linear models provide a useful class of tools for modeling complex data by naturally
incorporating a combination of linear and nonlinear effects within one framework. One key …

Motivated by recent work studying massive imaging data in the neuroimaging literature, we
propose multivariate varying coefficient models (MVCM) for modeling the relation between …

Nonparametric varying coefficient models are useful for studying the time-dependent effects
of variables. Many procedures have been developed for estimation and variable selection in …

The varying-coefficient model is flexible and powerful for modeling the dynamic changes of
regression coefficients. It is important to identify significant covariates associated with …