Jackknife model averaging for high-dimensional quantile regression

doi:10.1111/biom.13574

CSpace

	Jackknife model averaging for high-dimensional quantile regression
	Wang, Miaomiao 1,2,3; Zhang, Xinyu 2,4; Wan, Alan T. K.5; You, Kang 6; Zou, Guohua 6
	2021-10-28
发表期刊	BIOMETRICS
ISSN	0006-341X
页码	12
摘要	In this paper, we propose a frequentist model averaging method for quantile regression with high-dimensional covariates. Although research on these subjects has proliferated as separate approaches, no study has considered them in conjunction. Our method entails reducing the covariate dimensions through ranking the covariates based on marginal quantile utilities. The second step of our method implements model averaging on the models containing the covariates that survive the screening of the first step. We use a delete-one cross-validation method to select the model weights, and prove that the resultant estimator possesses an optimal asymptotic property uniformly over any compact (0,1) subset of the quantile indices. Our proof, which relies on empirical process theory, is arguably more challenging than proofs of similar results in other contexts owing to the high-dimensional nature of the problem and our relaxation of the conventional assumption of the weights summing to one. Our investigation of finite-sample performance demonstrates that the proposed method exhibits very favorable properties compared to the least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) penalized regression methods. The method is applied to a microarray gene expression data set.
关键词	asymptotic optimality high-dimensional quantile regression marginal quantile utility model averaging
DOI	10.1111/biom.13574
收录类别	SCI
语种	英语
资助项目	Ministry of Science and Technology of China[2020AAA0105200] ; Ministry of Science and Technology of China[2016YFB0502301] ; National Natural Science Foundation of China[71925007] ; National Natural Science Foundation of China[72091212] ; National Natural Science Foundation of China[71988101] ; National Natural Science Foundation of China[11688101] ; National Natural Science Foundation of China[71973116] ; National Natural Science Foundation of China[11971323] ; Hong Kong Research Grants Council[9042873] ; Fundamental Research Funds for the Central Universities[2020-JYB-XJSJJ-013]
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biology ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000711998600001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59493
专题	中国科学院数学与系统科学研究院
通讯作者	Zhang, Xinyu
作者单位	1.Beijing Univ Chinese Med, Sch Chinese Mat Med, Beijing, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Beijing Acad Artificial Intelligence, Beijing, Peoples R China 5.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China 6.Capital Normal Univ, Sch Math Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Miaomiao,Zhang, Xinyu,Wan, Alan T. K.,et al. Jackknife model averaging for high-dimensional quantile regression[J]. BIOMETRICS,2021:12.
APA	Wang, Miaomiao,Zhang, Xinyu,Wan, Alan T. K.,You, Kang,&Zou, Guohua.(2021).Jackknife model averaging for high-dimensional quantile regression.BIOMETRICS,12.
MLA	Wang, Miaomiao,et al."Jackknife model averaging for high-dimensional quantile regression".BIOMETRICS (2021):12.