KMS Of Academy of mathematics and systems sciences, CAS
| Jackknife model averaging for high-dimensional quantile regression | |
| Wang, Miaomiao1,2,3; Zhang, Xinyu2,4; Wan, Alan T. K.5; You, Kang6; Zou, Guohua6 | |
| 2021-10-28 | |
| Source Publication | BIOMETRICS
 |
| ISSN | 0006-341X |
| Pages | 12 |
| Abstract | 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. |
| Keyword | asymptotic optimality high-dimensional quantile regression marginal quantile utility model averaging |
| DOI | 10.1111/biom.13574 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics |
| WOS Subject | Biology ; Mathematical & Computational Biology ; Statistics & Probability |
| WOS ID | WOS:000711998600001 |
| Publisher | WILEY |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/59493 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Zhang, Xinyu |
| Affiliation | 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 |
| Recommended Citation 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. |
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