A New Class of Estimators Based on a General Relative Loss Function

Motivated by the relative loss estimator of the median, we propose a new class of estimators for linear quantile models using a general relative loss function defined by the Box-Cox transformation function. The proposed method is very flexible. It includes a traditional quantile regression and median regression under the relative loss as special cases. Compared to the traditional linear quantile estimator, the proposed estimator has smaller variance and hence is more efficient in making statistical inferences. We show that, in theory, the proposed estimator is consistent and asymptotically normal under appropriate conditions. Extensive simulation studies were conducted, demonstrating good performance of the proposed method. An application of the proposed method in a prostate cancer study is provided.

Automated summary

Motivated by the relative loss estimator of the median, a new class of estimators for linear quantile models is proposed using a general relative loss function. The proposed estimator is shown to have smaller variance and be more efficient than traditional linear quantile estimator. Simulation studies and application in a prostate cancer study demonstrate good performance of the proposed method.