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Smooth-Threshold GEE Variable Selection Based on Quadratic Inference Functions with Longitudinal Data

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Information Computing and Applications (ICICA 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 391))

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Abstract

A variable selection procedure is proposed using smooth-threshold generalized estimating equations based on quadratic inference functions (SGEE-QIF). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE-QIF. The proposed procedure avoids the convex optimization problem and is flexible and easy to implement. We establish the consistency and asymptotic normality of the resulting estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure.

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Author information

Authors and Affiliations

  1. College of Applied Sciences, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, 100124, P.R. China

    Ruiqin Tian & Liugen Xue

Authors
  1. Ruiqin Tian
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  2. Liugen Xue
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Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, 800 Dongchuan Road, Dianxinqunlou 1-401, 200240, Shanghai, China

    Yuhang Yang

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore, Singapore

    Maode Ma

  3. College of Science, Hebei United University, 063009, Tangshan, Hebei, China

    Baoxiang Liu

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© 2013 Springer-Verlag Berlin Heidelberg

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Tian, R., Xue, L. (2013). Smooth-Threshold GEE Variable Selection Based on Quadratic Inference Functions with Longitudinal Data. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53932-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-53932-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53931-2

  • Online ISBN: 978-3-642-53932-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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