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2021 Learning sparse conditional distribution: An efficient kernel-based approach
Fang Chen, Xin He, Junhui Wang
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Electron. J. Statist. 15(1): 1610-1635 (2021). DOI: 10.1214/21-EJS1824

Abstract

This paper proposes a novel method to recover the sparse structure of the conditional distribution, which plays a crucial role in subsequent statistical analysis such as prediction, forecasting, conditional distribution estimation and others. Unlike most existing methods that often require explicit model assumption or suffer from computational burden, the proposed method shows great advantage by making use of some desirable properties of reproducing kernel Hilbert space (RKHS). It can be efficiently implemented by optimizing its dual form and is particularly attractive in dealing with large-scale dataset. The asymptotic consistencies of the proposed method are established under mild conditions. Its effectiveness is also supported by a variety of simulated examples and a real-life supermarket dataset from Northern China.

Funding Statement

Xin He’s research is supported in part by NSFC-11901375, Shanghai Pujiang Program 2019PJC051 and the Fundamental Research Funds for the Central Universities. Junhui Wang’s research is supported in part by HK RGC Grants GRF-11303918, GRF-11300919 and GRF-11304520.

Acknowledgments

The authors thank the editor, the associate editor and the two anonymous referees for their constructive suggestions, which significantly improve this paper.

Citation

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Fang Chen. Xin He. Junhui Wang. "Learning sparse conditional distribution: An efficient kernel-based approach." Electron. J. Statist. 15 (1) 1610 - 1635, 2021. https://doi.org/10.1214/21-EJS1824

Information

Received: 1 April 2020; Published: 2021
First available in Project Euclid: 26 March 2021

Digital Object Identifier: 10.1214/21-EJS1824

Subjects:
Primary: 62G08, 68Q32
Secondary: 62J07

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Vol.15 • No. 1 • 2021
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