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Variable selection in robust regression models for longitudinal data

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  • Fan, Yali
  • Qin, Guoyou
  • Zhu, Zhongyi
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    Abstract

    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 oracle properties of the proposed robust variable selection methods. A simulation study shows the robustness of the proposed methods against outliers. Moreover, it is found by the simulation study that incorporating the correlation structure into the procedure of variable selection will lead to better performance than ignoring the correlation structure for longitudinal data. In the end, the proposed methods are illustrated in the analysis of a real data set.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 109 (2012)
    Issue (Month): C ()
    Pages: 156-167

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    Handle: RePEc:eee:jmvana:v:109:y:2012:i:c:p:156-167

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    Related research

    Keywords: Longitudinal data; Penalized estimating equation; Robust method; Variable selection;

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    Cited by:
    1. Jia Chen & Jiti Gao, 2014. "Semiparametric Model Selection in Panel Data Models with Deterministic Trends and Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 15/14, Monash University, Department of Econometrics and Business Statistics.

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