01.06.2015 | Original Research | Ausgabe 1-2/2015
Empirical likelihood for composite quantile regression modeling
- Zeitschrift:
- Journal of Applied Mathematics and Computing > Ausgabe 1-2/2015
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.
