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Local estimation for varying-coefficient models with longitudinal data

Author

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  • Hongmei Lin
  • Riquan Zhang
  • Jianhong Shi

Abstract

Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient models for longitudinal data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality for the proposed estimators of varying-coefficient functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.

Suggested Citation

  • Hongmei Lin & Riquan Zhang & Jianhong Shi, 2017. "Local estimation for varying-coefficient models with longitudinal data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(15), pages 7511-7528, August.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:15:p:7511-7528
    DOI: 10.1080/03610926.2016.1154156
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