MM algorithm of the estimation of single-index quantile regression

doi:10.7523/j.issn.2095-6134.2021.03.001

Abstract

Abstract: The single-index quantile regression model is an important semiparametric model with the merit of dimensionality reduction. Furthermore, it retains the robustness of a nonparametric model. For most existing estimating procedures of single-index quantile regression models, the estimators are obtained via minimizing the objective functions by the interior point method. In this paper, we investigate the MM (majorize-minimize) algorithm of the single index quantile regression model estimating procedure. We first construct the majorize function of the objective function and then minimize the substituted majorize function to find the estimators. Our numerical simulations and empirical study show that for the considered model, the MM algorithm has good stability and can yield more accurate estimation. Compared with the interior point method, the MM algorithm is more efficient and takes less time.

Key words: single-index quantile regression model, MM-algorithm, surrogate function, computational efficiency

CLC Number:

O212.4

GUO Yuanyuan, YANG Xuemei, SUN Zhihua. MM algorithm of the estimation of single-index quantile regression[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(3): 289-296.

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