A semi-parametric approach for mixture models: Application to local false discovery rate estimation |
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| Affiliation: | 1. UMR518 AgroParisTech/INRA, 16 rue Claude Bernard, 75005 Paris, France;2. Université Paris X, 200 avenue de la République, 92001 Nanterre Cedex, France;1. School of Forensic Medicine, China Medical University, No. 92, Beier Road, Heping District, Shenyang, Liaoning 110001, China;2. Department of Forensic Medicine, National Police University of China, No. 38, Tawan Street, Huanggu District, Shenyang, Liaoning 110854, China;3. Criminal Police Brigade of Huanggu Branch Bureau, Shenyang Public Security Bureau, No. 232, Huahan Road, Huanggu District, Shenyang, Liaoning 110036, China;4. Department of Forensic Medicine, National Police University of China, No. 38, Tawan Street, Huanggu District, Shenyang, Liaoning 110854, China;5. Institute of Forensic Science, Guizhou Public Security Department, No.189, Jianlongdong Road, Nanming District, Guiyang, Guizhou 550005, China;1. West China School of Basic Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China;2. Institute of Forensic Science, Chengdu Public Security Bureau, Chengdu, Sichuan, China;1. Department of Forensic Genetics, West China School of Basic Science and Forensic Medicine, Sichuan University, 3-17 Renmin South Road, Chengdu 610041, Sichuan, China;2. Department of Forensic Genetics, Institute of Forensic Science, Chengdu Public Security Bureau, Chengdu 610081, Sichuan, China;3. College of Life Sciences, Sichuan University, Chengdu 610041, Sichuan, China;4. Bio-resources Key Laboratory of Minister of Education, Sichuan University, Chengdu 610041, Sichuan, China;1. CIFASIS, Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas, Ocampo y Esmeralda, S2000EZP Rosario, Argentina;2. FCEIA – UNR, Rosario, Argentina;3. Aix Marseille Université, CNRS, ENSAM, Université de Toulon, LSIS UMR 7296, 13397, Marseille, France |
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| Abstract: | A procedure to estimate a two-component mixture model where one component is known is proposed. The unknown part is estimated with a weighted kernel function. The weights are defined in an adaptive way. The convergence to a unique solution of our estimation procedure is proven. The procedure is compared with two classical approaches using simulation. In addition, the results obtained are applied to multiple testing procedure in order to estimate the posterior population probabilities and the local false discovery rate. |
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