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Empirical likelihood for composite quantile regression modeling

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Abstract

This paper studies empirical likelihood inferences via composite quantile regression. It is shown that the proposed empirical log-likelihood ratio is asymptotically chi-squared, and then the confidence intervals for the regression coefficients are constructed. The proposed method avoids estimating the unknown error density function involved in the asymptotic covariance matrix of the estimators. Some simulation studies indicate that the proposed empirical likelihood procedure is more efficient and robust than the normal approximation method.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China under Grant No. 11101119 and 11301569, the Program for Excellent Young Teachers in Guangxi University, and the Philosophy and Social sciences Foundation of Guangxi and Grant No. 11FTJ002.

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Authors and Affiliations

  1. College of Mathematics and statistics, Hechi University, Yizhou,  546300, Guangxi, China

    Peixin Zhao

  2. College of Mathematics and Science, Dezhou University, Dezhou,  253023, Shandong, China

    Xiaoshuang Zhou

  3. School of Mathematics, Shandong University, Jinan,  250014, Shandong, China

    Lu Lin

Authors
  1. Peixin Zhao
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  2. Xiaoshuang Zhou
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  3. Lu Lin
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Correspondence to Peixin Zhao.

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Zhao, P., Zhou, X. & Lin, L. Empirical likelihood for composite quantile regression modeling. J. Appl. Math. Comput. 48, 321–333 (2015). https://doi.org/10.1007/s12190-014-0804-3

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  • DOI: https://doi.org/10.1007/s12190-014-0804-3

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