This article develops a mean field variational Bayes approximation algorithm for posterior inferences of the recently proposed partially linear additive models with simultaneous and automatic variable selection and linear/nonlinear component identification abilities. To solve the problem induced...

We consider the problem of variable selection for the generalized linear models (GLMs) with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold generalized estimating equations (SGEE). The proposed procedure automatically eliminates inactive...

We propose an empirical likelihood method for application to a partially linear panel data model with fixed effects. The empirical log-likelihood ratio statistic is proved to be asymptotically chi-squared distributed, and the asymptotic properties of estimators for both the parametric and...

In this paper, we consider the statistical inference for the partially liner varying coefficient model with measurement error in the nonparametric part when some prior information about the parametric part is available. The prior information is expressed in the form of exact linear restrictions....

The empirical likelihood method is especially useful for constructing confidence intervals or regions of parameters of interest. Yet, the technique cannot be directly applied to partially linear single-index models for longitudinal data due to the within-subject correlation. In this paper, a...

In this paper, we consider the problem of variable selection for partially varying coefficient single-index model, and present a regularized variable selection procedure by combining basis function approximations with smoothly clipped absolute deviation penalty. The proposed procedure...