Robust Semiparametric Optimal Testing Procedure for Multiple Normal Means Liu, Peng Wang, Chong Wang, Chong
dc.contributor.department Statistics
dc.contributor.department Veterinary Diagnostic and Production Animal Medicine 2018-02-17T17:45:44.000 2020-07-07T05:13:26Z 2020-07-07T05:13:26Z Sun Jan 01 00:00:00 UTC 2012 2012-01-01
dc.description.abstract <p>In high-dimensional gene expression experiments such as microarray and RNA-seq experiments, the number of measured variables is huge while the number of replicates is small. As a consequence, hypothesis testing is challenging because the power of tests can be very low after controlling multiple testing error. Optimal testing procedures with high average power while controlling false discovery rate are preferred. Many methods were constructed to achieve high power through borrowing information across genes. Some of these methods can be shown to achieve the optimal average power across genes, but only under a normal assumption of alternative means. However, the assumption of a normal distribution is likely violated in practice. In this paper, we propose a novel semiparametric optimal testing (SPOT) procedure for high-dimensional data with small sample size. Our procedure is more robust because it does not depend on any parametric assumption for the alternative means. We show that the proposed test achieves the maximum average power asymptotically as the number of tests goes to infinity. Both simulation study and the analysis of a real microarray data with spike-in probes show that the proposed SPOT procedure performs better when compared to other popularly applied procedures.</p>
dc.description.comments <p>This article is from <em>Journal of Probability and Statistics</em> 2012 (2012); 913560, doi: <a href="" target="_blank">10.1155/2012/913560</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1062
dc.identifier.contextkey 8689684
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath vdpam_pubs/48
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 00:27:07 UTC 2022
dc.source.uri 10.1155/2012/913560
dc.subject.disciplines Other Statistics and Probability
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Veterinary Physiology
dc.title Robust Semiparametric Optimal Testing Procedure for Multiple Normal Means
dc.type article
dc.type.genre article
dspace.entity.type Publication
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