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Estimation of semi-varying coefficient models for longitudinal data with irregular error structure

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  • Zhao, Yan-Yong
  • Lin, Jin-Guan
  • Zhao, Jian-Qiang
  • Miao, Zhang-Xiao

Abstract

Semiparametric models are often considered for modeling longitudinal data for a good balance between flexibility and parsimony. In this paper, we focus on the estimation for a longitudinal semi-varying coefficient model which is of irregular errors. A semiparametric profile least-squares method is developed to estimate parameters in the mean function and error structure simultaneously. Then, a two-stage local linear estimator is investigated for the nonparametric part. Further, we establish the asymptotic properties of the resulting estimators under some mild conditions. The practical problems of implementation are also addressed. Finally, three numerical experiments are conducted to verify the finite sample performance of the proposed methods, and an application to the CD4 cell data is provided for illustration.

Suggested Citation

  • Zhao, Yan-Yong & Lin, Jin-Guan & Zhao, Jian-Qiang & Miao, Zhang-Xiao, 2022. "Estimation of semi-varying coefficient models for longitudinal data with irregular error structure," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:csdana:v:169:y:2022:i:c:s0167947321002231
    DOI: 10.1016/j.csda.2021.107389
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    References listed on IDEAS

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