Jackknife model averaging for high-dimensional quantile regression
文献类型：期刊论文
| 作者 | Wang, Miaomiao1,2,3; Zhang, Xinyu2,4; Wan, Alan T. K.5; You, Kang6; Zou, Guohua6 |
| 刊名 | BIOMETRICS
 |
| 出版日期 | 2021-10-28 |
| 页码 | 12 |
| 关键词 | asymptotic optimality high-dimensional quantile regression marginal quantile utility model averaging |
| ISSN号 | 0006-341X |
| DOI | 10.1111/biom.13574 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000711998600001 |
| 出版者 | WILEY |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/59492]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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