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The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.<\/jats:p>","DOI":"10.1111\/rssb.12258","type":"journal-article","created":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T11:32:39Z","timestamp":1508758359000},"page":"433-452","source":"Crossref","is-referenced-by-count":21,"title":["Testing for Marginal Linear Effects in Quantile Regression"],"prefix":"10.1093","volume":"80","author":[{"given":"Huixia Judy","family":"Wang","sequence":"first","affiliation":[{"name":"George Washington University , Washington DC , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian W.","family":"McKeague","sequence":"additional","affiliation":[{"name":"Columbia University , New York , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Qian","sequence":"additional","affiliation":[{"name":"Columbia University , New York , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2017,10,23]]},"reference":[{"key":"2023022101103959400_","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1111\/j.1468-0262.2006.00671.x","article-title":"Quantile regression under misspecification, with an application to the U.S. wage structure","volume":"74","author":"Angrist","year":"2006","journal-title":"Econometrica"},{"key":"2023022101103959400_","article-title":"Valid post-selection inference in high-dimensional approximately sparse quantile regression models","volume-title":"Technical Report","author":"Belloni","year":"2014"},{"key":"2023022101103959400_","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1093\/biomet\/asu056","article-title":"Uniform post selection inference for LAD regression and other Z-estimation problems","volume":"102","author":"Belloni","year":"2015","journal-title":"Biometrika"},{"key":"2023022101103959400_","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1111\/rssb.12034","article-title":"Two-sample test of high dimensional means under dependence","volume":"76","author":"Cai","year":"2014","journal-title":"J. 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