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Semiparametric Hierarchical Composite Quantile Regression

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  • Yanliang Chen
  • Man-Lai Tang
  • Maozai Tian

Abstract

In biological, medical, and social sciences, multilevel structures are very common. Hierarchical models that take the dependencies among subjects within the same level are necessary. In this article, we introduce a semiparametric hierarchical composite quantile regression model for hierarchical data. This model (i) keeps the easy interpretability of the simple parametric model; (ii) retains some of the flexibility of the complex non parametric model; (iii) relaxes the assumptions that the noise variances and higher-order moments exist and are finite; and (iv) takes the dependencies among subjects within the same hierarchy into consideration. We establish the asymptotic properties of the proposed estimators. Our simulation results show that the proposed method is more efficient than the least-squares-based method for many non normally distributed errors. We illustrate our methodology with a real biometric data set.

Suggested Citation

  • Yanliang Chen & Man-Lai Tang & Maozai Tian, 2015. "Semiparametric Hierarchical Composite Quantile Regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(5), pages 996-1012, March.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:5:p:996-1012
    DOI: 10.1080/03610926.2012.755199
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    Cited by:

    1. Tian, Yuzhu & Zhu, Qianqian & Tian, Maozai, 2016. "Estimation of linear composite quantile regression using EM algorithm," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 183-191.
    2. Feng, Xingdong & Liu, Qiaochu & Wang, Caixing, 2023. "A lack-of-fit test for quantile regression process models," Statistics & Probability Letters, Elsevier, vol. 192(C).

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