Efficient estimation for marginal generalized partially linear single-index models with longitudinal data

Peirong Xu (), Jun Zhang (), Xingfang Huang () and Tao Wang ()
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Peirong Xu: Southeast University
Jun Zhang: Shenzhen University
Xingfang Huang: Southeast University
Tao Wang: Yale University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2016, vol. 25, issue 3, No 2, 413-431

Abstract: Abstract We consider marginal generalized partially linear single-index models for longitudinal data. A profile generalized estimating equations (GEE)-based approach is proposed to estimate unknown regression parameters. Within a wide range of bandwidths for estimating the nonparametric function, our profile GEE estimator is consistent and asymptotically normal even if the covariance structure is misspecified. Moreover, if the covariance structure is correctly specified, the semiparametric efficiency can be achieved under heteroscedasticity and without distributional assumptions on the covariates. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedure. The proposed methodology is further illustrated through a data analysis.

Keywords: Generalized partially single-index models; Generalized estimating equations; Longitudinal data; Semiparametric efficiency; 62-02; 62F12; 62G08 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11749-015-0462-2

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