Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data

Peng Lai, Qihua Wang and Heng Lian

Journal of Multivariate Analysis, 2012, vol. 105, issue 1, 422-432

Abstract: In this paper, we present an estimation approach based on generalized estimating equations and a variable selection procedure for single-index models when the observed data are clustered. Unlike the case of independent observations, bias-correction is necessary when general working correlation matrices are used in the estimating equations. Our variable selection procedure based on smooth-threshold estimating equations (Ueki (2009) [23]) can automatically eliminate irrelevant parameters by setting them as zeros and is computationally simpler than alternative approaches based on shrinkage penalty. The resulting estimator consistently identifies the significant variables in the index, even when the working correlation matrix is misspecified. The asymptotic property of the estimator is the same whether or not the nonzero parameters are known (in both cases we use the same estimating equations), thus achieving the oracle property in the sense of Fan and Li (2001) [10]. The finite sample properties of the estimator are illustrated by some simulation examples, as well as a real data application.

Keywords: Generalized estimating equation; Longitudinal data; Oracle property; Single-index model; Variable selection (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.jmva.2011.08.009

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