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In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and B\u00fchlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi mathvariant=\"normal\">\u039b<\/mml:mi><\/mml:mrow><\/mml:math>in lasso and the parameter<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:msub><mml:mrow><mml:mi>\u03bb<\/mml:mi><\/mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">m<\/mml:mi><mml:mi mathvariant=\"normal\">i<\/mml:mi><mml:mi mathvariant=\"normal\">n<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:mi mathvariant=\"normal\">\u039b<\/mml:mi><\/mml:mrow><\/mml:math>and<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:msub><mml:mrow><mml:mi>\u03bb<\/mml:mi><\/mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">m<\/mml:mi><mml:mi mathvariant=\"normal\">i<\/mml:mi><mml:mi mathvariant=\"normal\">n<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.<\/jats:p>","DOI":"10.1155\/2017\/2747431","type":"journal-article","created":{"date-parts":[[2017,11,15]],"date-time":"2017-11-15T23:30:52Z","timestamp":1510788652000},"page":"1-10","source":"Crossref","is-referenced-by-count":11,"title":["Ensembling Variable Selectors by Stability Selection for the Cox Model"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1583-1523","authenticated-orcid":true,"given":"Qing-Yan","family":"Yin","sequence":"first","affiliation":[{"name":"School of Science, Xi\u2019an University of Architecture and Technology, Xi\u2019an, Shaanxi 710055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Li","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi 710049, 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