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Single-index composite quantile regression for massive data

Rong Jiang and Keming Yu

Journal of Multivariate Analysis, 2020, vol. 180, issue C

Abstract: Composite quantile regression (CQR) is becoming increasingly popular due to its robustness from quantile regression. Recently, the CQR method has been studied extensively with single-index models. However, the numerical inference of CQR methods for single-index models must involve iteration. In this study, we propose a non-iterative CQR (NICQR) estimation algorithm and derive the asymptotic distribution of the proposed estimator. Moreover, we extend the NICQR method to the analysis of massive datasets via a divide-and-conquer strategy. The proposed approach significantly reduces the computing time and the required primary memory. Simulation studies and two real data applications are conducted to illustrate the finite sample performance of the proposed methods.

Keywords: Composite quantile regression; Massive data; Single-index model (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.jmva.2020.104669

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