Variable selection for generalized varying coefficient models with longitudinal data

Hu Yang, Chaohui Guo () and Jing Lv

Statistical Papers, 2016, vol. 57, issue 1, 115-132

Abstract: In this paper, we apply the penalized quadratic inference function to perform variable selection and estimation simultaneously for generalized varying coefficient models with longitudinal data. The proposed approach is based on basis function approximations and the group SCAD penalty, which can incorporate information on the correlation structure within the same subject to achieve an efficient estimator. Furthermore, we discuss the asymptotic theory of our proposed procedure under suitable conditions, including consistency in variable selection and the oracle property in estimation. Finally, monte carlo simulations and a real data analysis are conducted to examine the finite sample performance of the proposed procedure. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Generalized varying coefficient models; Longitudinal data; Quadratic inference function; Group SCAD penalty; Splines; Variable selection (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s00362-014-0647-x

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