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In addition to the problem of dimension, the data might also be contaminated. There are two main types of contamination: outliers and model misspecification. We develop an unique method that takes into account the ultrahigh or high dimensional issues and both types of contamination. In this article, we propose a framework for feature screening and selection based on the minimum Lq\u2010likelihood estimation (MLqE), which accounts for the model misspecification contamination issue and has also been shown to be robust to outliers. In numerical analysis, we explore the robustness of this framework under different outliers and model misspecification scenarios. To examine the performance of this framework, we conduct real data analysis using the skin cutaneous melanoma data. When comparing with traditional screening and feature selection methods, the proposed method shows superiority in both variable identification effectiveness and parameter estimation accuracy.<\/jats:p>","DOI":"10.1002\/sim.9563","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T09:11:29Z","timestamp":1663060289000},"page":"5220-5241","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lq\u2010based robust analytics on ultrahigh and high dimensional data"],"prefix":"10.1002","volume":"41","author":[{"given":"Jiachen","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Biostatistics Boston University  Boston MA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruofan","family":"Bie","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Brown University  Providence RI USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichen","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Operations, Business Analytics and Information Systems University of Cincinnati  Cincinnati OH USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6287-5094","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics and School of Statistics Renmin University of China  Beijing China"},{"name":"RSS and China\u2010Re Life Joint Lab on Public Health and Risk Management Renmin University of China  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangge","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Boston University  Boston MA USA"},{"name":"RSS and China\u2010Re Life Joint Lab on Public Health and Risk Management Renmin University of China  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab256"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.tm10563"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1235381"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2008.00674.x"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOS798"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.tm09779"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11425-015-5062-9"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000735"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382273"},{"key":"e_1_2_8_12_1","first-page":"237","volume-title":"Outlier Analysis","author":"Aggarwal CC","year":"2015"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3966"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.3980070102"},{"issue":"6","key":"e_1_2_8_15_1","first-page":"241","article-title":"Identification of outliers in medical diagnostic system using data mining techniques","volume":"4","author":"Deneshkumar V","year":"2014","journal-title":"Int J Stat"},{"key":"e_1_2_8_16_1","unstructured":"RenJ ZhouF LiX MaS JiangY WuC.Robust Bayesian variable selection for gene\u2010environment interactions. arXiv preprint arXiv:2006.05455 2020."},{"key":"e_1_2_8_17_1","unstructured":"FlickingerM.Detecting and correcting contamination in genetic data.2016. 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