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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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