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Bayesian inference for the proportion of true null hypotheses using minimum Hellinger distance

Authors
Kang, MoonsuLee, Jaewon
Issue Date
4월-2012
Publisher
ELSEVIER SCIENCE BV
Keywords
Proportion of true null; Strong dependence; Dirichlet process; Minimum Hellinger distance; IWMDE; Microarray
Citation
JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.142, no.4, pp.820 - 825
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume
142
Number
4
Start Page
820
End Page
825
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/108899
DOI
10.1016/j.jspi.2011.10.001
ISSN
0378-3758
Abstract
It is important that the proportion of true null hypotheses be estimated accurately in a multiple hypothesis context. Current estimation methods, however, are not suitable for high-dimensional data such as microarray data. First, they do not consider the (strong) dependence between hypotheses (or genes), thereby resulting in inaccurate estimation. Second, the unknown distribution of false null hypotheses cannot be estimated properly by these methods. Third, the estimation is affected strongly by outliers. In this paper, we find out the optimal procedure for estimating the proportion of true null hypotheses under a (strong) dependence based on the Dirichlet process prior. In addition, by using the minimum Hellinger distance, the estimation should be robust to any model misspecification as well as to any outliers while maintaining efficiency. The results are confirmed by a simulation study, and the newly developed methodology is illustrated by a real microarray data. (C) 2011 Elsevier B.V. All rights reserved.
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