Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98050
Title: Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data
Authors: Lai, Peng
Wang, Qihua
Lian, Heng
Keywords: DRNTU::Science::Mathematics::Analysis
Issue Date: 2011
Source: Lai, P., Wang, Q., & Lian, H. (2011). Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data. Journal of multivariate analysis, 105(1), 422-432.
Series/Report no.: Journal of multivariate analysis
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.
URI: https://hdl.handle.net/10356/98050
http://hdl.handle.net/10220/17499
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2011.08.009
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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