Kernel density regression

Chen, Xin1; Ma, Xuejun2; Zhou, Wang3

2020-03

10.1016/j.jspi.2019.09.001

Journal of Statistical Planning and Inference   影响因子和分区

0378-3758

1873-1171

Linear regression is a fundamental and popular statistical method. There are various kinds of linear regression, such as mean regression and quantile regression. In this paper, we propose a new one called kernel density regression, which allows broad-spectrum of the error distribution in the linear regression. Our studies indicate that our method provides a better alternative than mean regression and quantile regression under many settings, particularly for asymmetrical heavy-tailed distribution or multimodal distribution of the error term. Furthermore, penalized likelihood estimator is proposed. Numerical studies also demonstrate the effectiveness and the flexibility of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.

Kernel density regression Kernel density Linear regression

SCI

英语

第一

National University of Singapore[R-155-000-204-114]

Mathematics

Statistics & Probability

WOS:000491633800023

ELSEVIER

Web of Science

1.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen 518055, Peoples R China
2.Soochow Univ, Sch Math Sci, Suzhou 215006, Peoples R China
3.Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117546, Singapore

Chen, Xin,Ma, Xuejun,Zhou, Wang. Kernel density regression[J]. Journal of Statistical Planning and Inference,2020,205:318-329.

Chen, Xin,Ma, Xuejun,&Zhou, Wang.(2020).Kernel density regression.Journal of Statistical Planning and Inference,205,318-329.

Chen, Xin,et al."Kernel density regression".Journal of Statistical Planning and Inference 205(2020):318-329.