The varying coefficient models are very important tool to explore the dynamic pattern in
many scientific areas, such as economics, finance, politics, epidemiology, medical science …

The complexity of semiparametric models poses new challenges to statistical inference and
model selection that frequently arise from real applications. In this work, we propose new …

In this paper, we are concerned with how to select significant variables in semiparametric
modeling. Variable selection for semiparametric regression models consists of two …

Improving efficiency for regression coefficients and predicting trajectories of individuals are
two important aspects in the analysis of longitudinal data. Both involve estimation of the …

This article focuses on variable selection for partially linear models when the covariates are
measured with additive errors. We propose two classes of variable selection procedures …

In the last decade, significant theoretical advances have been made in the area of
frequentist model averaging (FMA); however, the majority of this work has emphasized …

We consider parameter estimation, hypothesis testing and variable selection for partially
time-varying coefficient models. Our asymptotic theory has the useful feature that it can allow …

The generalized varying coefficient partially linear model with growing number of predictors
arises in many contemporary scientific endeavor. In this paper we set foot on both theoretical …

Identification of constant coefficients in a semi-varying coefficient model is an important
issue (Zhang et al (2002)). We propose a novel method for this by combining local …

Nonparametric independence screening and structure identification for ultra-high dimensional
longitudinal data Page 1 The Annals of Statistics 2014, Vol. 42, No. 5, 1819–1849 DOI …