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A0291
Title: A semiparametric mixture method for local false discovery rate estimation Authors:  Woncheol Jang - Seoul National University (Korea, South) [presenting]
Abstract: A two-component semiparametric mixture model is proposed to estimate local false discovery rates in multiple testing problems. The two pillars of the proposed approach are Efron's empirical null principle and log-concave density estimation for the alternative distribution. Our method outperforms other existing methods, in particular when the proportion of null is not that high. It is robust against the misspecification of alternative distribution. A unique feature of our method is that it can be extended to compute the local false discovery rates by combining multiple lists of p-values. We demonstrate the strengths of the proposed method by simulation and several case studies.