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Penalized Quantile Regression for Distributed Big Data Using the Slack Variable Representation

Penalized quantile regression is a widely used tool for analyzing high-dimensional data with heterogeneity. Although its estimation theory has been well studied in the literature, its computation still remains a challenge in big data, due to the nonsmoothness of the check loss function and the possible nonconvexity of the penalty term. In this article, we propose the QPADM-slack method, a parallel algorithm formulated via the alternating direction method of multipliers (ADMM) that supports penalized quantile regression in big data. Our proposal is different from the recent QPADM algorithm and uses the slack variable representation of the quantile regression problem. Simulation studies demonstrate that this new formulation is significantly faster than QPADM, especially when the data volume n or the dimension p is large, and has favorable estimation accuracy in big data analysis for both nondistributed and distributed environments. We further illustrate the practical performance of QPADM-slack by analyzing a news popularity dataset.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.30 No.3 September 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 557 - 565

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

QUANTILE REGRESSION
BIG DATA
ADMM
NONCONVEX PENALTY

Info Detail Spesifik

https://doi.org/10.1080/10618600.2020.1840996

Pernyataan Tanggungjawab

Ye Fan, Nan Lin, Xianjun Yin

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