This paper introduces the robust empirical likelihood (REL) inference for the longitudinal data. We propose the REL method by constructing robust auxiliary random vectors, and employ bounded scores and leverage-based weights in the auxiliary random vectors to achieve robustness against outliers...

In this paper, we consider robust generalized estimating equations for the analysis of semiparametric generalized partial linear mixed models (GPLMMs) for longitudinal data. We approximate the non-parametric function in the GPLMM by a regression spline, and make use of bounded scores and...

In this article, we consider variable selection in robust regression models for longitudinal data. We propose a penalized robust estimating equation to estimate the regression parameters and to select the important covariate variables simultaneously. Under some regularity conditions, we show the...

Breast cancer is the most common non-skin cancer in women and the second most common cause of cancer-related death in US women. It is well known that the breast cancer survival rate varies with age at diagnosis. For most cancers, the relative survival rate decreases with age, but breast cancer...

In this paper, we develop robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE). The proposed approach integrates the robust method and joint mean–covariance regression modeling. Robust...

In this paper, we develop a new variable selection procedure for quantile varying coefficient models with longitudinal data. The proposed method is based on basis function approximation and a class of group versions of the adaptive LASSO penalty, which penalizes the Lγ norm of the within-group...