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Weighted quantile regression in varying-coefficient model with longitudinal data

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  • Lin, Fangzheng
  • Tang, Yanlin
  • Zhu, Zhongyi

Abstract

A weighted approach is developed to improve estimation efficiency in varying-coefficient quantile regression model, with longitudinal data. The weights are obtained from empirical likelihood of varying-coefficient mean model, where the nonparametric functions are approximated by basis splines, and the matrix expansion idea in quadratic inference function method is used, to model the inverse of conditional correlation matrix within subject. Theoretical results show that, the weighted estimators of the varying coefficients in quantile regression, can achieve higher efficiency than conventional estimators without weighting scheme. Simulation studies are used to assess the finite sample performance and a real data analysis is also conducted.

Suggested Citation

  • Lin, Fangzheng & Tang, Yanlin & Zhu, Zhongyi, 2020. "Weighted quantile regression in varying-coefficient model with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:csdana:v:145:y:2020:i:c:s0167947320300062
    DOI: 10.1016/j.csda.2020.106915
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    References listed on IDEAS

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    Cited by:

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    2. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).

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