{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T09:23:36Z","timestamp":1770542616778,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671633"],"award-info":[{"award-number":["81671633"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Sufficient variable screening rapidly reduces dimensionality with high probability in ultra-high dimensional modeling. To rapidly screen out the null predictors, a quantile-adaptive sufficient variable screening framework is developed by controlling the false discovery. Without any specification of an actual model, we first introduce a compound testing procedure based on the conditionally imputing marginal rank correlation at different quantile levels of response to select active predictors in high dimensionality. The testing statistic can capture sufficient dependence through two paths: one is to control false discovery adaptively and the other is to control the false discovery rate by giving a prespecified threshold. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.<\/jats:p>","DOI":"10.3390\/e25030524","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T04:05:23Z","timestamp":1679285123000},"page":"524","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantile-Adaptive Sufficient Variable Screening by Controlling False Discovery"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7213-1283","authenticated-orcid":false,"given":"Zihao","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Statistics, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqing","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Statistics, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5902-5021","authenticated-orcid":false,"given":"Han","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangxin","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of South Florida, Tampa, FL 33612, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","article-title":"Sure independence screening for ultrahigh dimensional feature space","volume":"70","author":"Fan","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B-Stat. Methodol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fuleky, P. (2020). Macroeconomic Forecasting in the Era of Big Data: Theory and Practice, Springer.","DOI":"10.1007\/978-3-030-31150-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3567","DOI":"10.1214\/10-AOS798","article-title":"Sure Independence Screening in Generalized Linear Models with Np-Dimensionality","volume":"38","author":"Fan","year":"2010","journal-title":"Ann. Stat."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1198\/jasa.2011.tm09779","article-title":"Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models","volume":"106","author":"Fan","year":"2011","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1214\/12-AOS1024","article-title":"Robust Rank Correlation Based Screening","volume":"40","author":"Li","year":"2012","journal-title":"Ann. Stat."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2123","DOI":"10.1214\/13-AOS1139","article-title":"Marginal Empirical Likelihood In addition, Sure Independence Feature Screening","volume":"41","author":"Chang","year":"2013","journal-title":"Ann. Stat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1198\/jasa.2011.tm10563","article-title":"Model-Free Feature Screening for Ultrahigh-Dimensional Data","volume":"106","author":"Zhu","year":"2011","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1080\/01621459.2012.695654","article-title":"Feature Screening via Distance Correlation Learning","volume":"107","author":"Li","year":"2012","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_9","first-page":"342","article-title":"Quantile-Adaptive Model-Free Variable Screening for High-Dimensional Heterogeneous Data","volume":"41","author":"He","year":"2013","journal-title":"Ann. Stat."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.csda.2013.05.016","article-title":"Nonparametric feature screening","volume":"67","author":"Lin","year":"2013","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00362-017-0931-7","article-title":"Model-free conditional screening via conditional distance correlation","volume":"61","author":"Lu","year":"2020","journal-title":"Stat. Pap."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1093\/biomet\/ass062","article-title":"The Kolmogorov filter for variable screening in high-dimensional binary classification","volume":"100","author":"Mai","year":"2013","journal-title":"BIOMETRIKA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/07350015.2013.863158","article-title":"Feature Screening for Ultrahigh Dimensional Categorical Data with Applications","volume":"32","author":"Huang","year":"2014","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1080\/01621459.2014.920256","article-title":"Model-Free Feature Screening for Ultrahigh Dimenssional Discriminant Analysis","volume":"110","author":"Cui","year":"2015","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1214\/18-AOS1738","article-title":"Nonparametric screening under conditional strictly convex loss for ultrahigh dimensional sparse data","volume":"47","author":"Han","year":"2019","journal-title":"Ann. Stat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1080\/01621459.2019.1632078","article-title":"Model-free forward screening via cumulative divergence","volume":"115","author":"Zhou","year":"2020","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1080\/01621459.2019.1573734","article-title":"Category-Adaptive Variable Screening for Ultra-High Dimensional Heterogeneous Categorical Data","volume":"115","author":"Xie","year":"2020","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/00031305.2016.1264311","article-title":"A note on high-dimensional linear regression with interactions","volume":"71","author":"Hao","year":"2017","journal-title":"Am. Stat."},{"key":"ref_19","first-page":"1801","article-title":"Quantile Correlation Based Variable Selection","volume":"40","author":"Tang","year":"2021","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1080\/01621459.2020.1783274","article-title":"Model-free feature screening and fdr control with knockoff features","volume":"117","author":"Liu","year":"2022","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, X., Ren, H., Zou, C., and Li, R. (2022). Threshold selection in feature screening for error rate control. J. Am. Stat. Assoc., 1\u201313.","DOI":"10.1080\/01621459.2021.2011735"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1214\/009053604000000292","article-title":"Testing predictor contributions in sufficient dimension reduction","volume":"32","author":"Cook","year":"2004","journal-title":"Ann. Stat."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1111\/rssb.12093","article-title":"Sequential Sufficient Dimension Reduction for Large p, Small n Problems","volume":"77","author":"Yin","year":"2015","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107530","DOI":"10.1016\/j.csda.2022.107530","article-title":"Independence index sufficient variable screening for categorical responses","volume":"174","author":"Yuan","year":"2022","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1080\/00031305.1996.10473566","article-title":"Sample Quantiles in Statistical Packages","volume":"50","author":"Hyndman","year":"1996","journal-title":"Am. Stat."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s13171-016-0088-9","article-title":"Approximation by Normal Distribution for A Sample Sum in Sampling Without Replacement from a Finite Population","volume":"78","author":"Mohamed","year":"2016","journal-title":"Sankhya A"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling The False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8143","DOI":"10.1038\/s41598-022-12311-4","article-title":"Predefined and data driven CT densitometric features predict critical illness and hospital length of stay in COVID-19 patients","volume":"12","author":"Shalmon","year":"2022","journal-title":"Sci. Rep."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/3\/524\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:58:02Z","timestamp":1760122682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/3\/524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":28,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["e25030524"],"URL":"https:\/\/doi.org\/10.3390\/e25030524","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,17]]}}}