A semi-parametric approach for mixture models: Application to local false discovery rate estimation

Authors:
Stéphane Robin;Avner Bar-Hen;Jean-Jacques Daudin;Laurent Pierre
Affiliations:
UMR518 AgroParisTech/INRA, 16 rue Claude Bernard, 75005 Paris, France;UMR518 AgroParisTech/INRA, 16 rue Claude Bernard, 75005 Paris, France;UMR518 AgroParisTech/INRA, 16 rue Claude Bernard, 75005 Paris, France;Université Paris X, 200 avenue de la République, 92001 Nanterre Cedex, France
Venue:
Computational Statistics & Data Analysis
Year:
2007

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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.