Variable selection and estimation for partially linear single-index models with longitudinal data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

39 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)579-593
Journal / PublicationStatistics and Computing
Volume25
Issue number3
Online published25 Feb 2014
Publication statusPublished - May 2015
Externally publishedYes

Abstract

In this paper, we consider the partially linear single-index models with longitudinal data. To deal with the variable selection problem in this context, we propose a penalized procedure combined with two bias correction methods, resulting in the bias-corrected generalized estimating equation and the bias-corrected quadratic inference function, which can take into account the correlations. Asymptotic properties of these methods are demonstrated. We also evaluate the finite sample performance of the proposed methods via Monte Carlo simulation studies and a real data analysis.

Research Area(s)

  • Bias correction, Longitudinal data, Partially linear single-index model, Variable selection