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The goal is to select which predictors affect any of the responses, where the number of such informative predictors tends to infinity as the sample size increases. There are marginal likelihoods for each experiment; we specify a pseudolikelihood combining the marginal likelihoods, and propose a pseudolikelihood information criterion. Under regularity conditions, we establish selection consistency for this criterion with unbounded true model size. The proposed method includes a Bayesian information criterion with appropriate penalty term as a special case. Simulations indicate that data integration can dramatically improve upon using only one data source.<\/jats:p>","DOI":"10.1093\/biomet\/asx023","type":"journal-article","created":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T19:21:47Z","timestamp":1490815307000},"page":"251-272","source":"Crossref","is-referenced-by-count":24,"title":["Data integration with high dimensionality"],"prefix":"10.1093","volume":"104","author":[{"given":"Xin","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada xingao@mathstat.yorku.ca"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond J.","family":"Carroll","sequence":"additional","affiliation":[{"name":"Department of Statistics, 447 Blocker Building, Texas A&M University, College Station, Texas 77843, U.S.A. carroll@stat.tamu.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,5,9]]},"reference":[{"key":"2019071815501344400_B1","first-page":"1179","article-title":"Consistency of the group lasso and multiple kernel learning.","volume":"9","author":"Bach","year":"2008","journal-title":"J. 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