期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 112, 期 25, 页码 7629-7634出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1507583112
关键词
inference; P values; lasso
资金
- National Science Foundation [DMS-9971405]
- National Institutes of Health [N01-HV-28183]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1208857] Funding Source: National Science Foundation
We describe the problem of selective inference. This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have cherry-picked-searched for the strongest associations-means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.
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