Generalized EM estimation for semi-parametric mixture distributions with discretized non-parametric component

Jun Ma*, Sigurbjorg Gudlaugsdottir, Graham Wood

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We consider independent sampling from a two-component mixture distribution, where one component (called the parametric component) is from a known distributional family and the other component (called the non-parametric component) is unknown. This is a semi-parametric mixture distribution. We discretize the non-parametric component and estimate the parameters of this mixture model, namely the mixing proportion, the unknown parameters of the parametric component and the discretized non-parametric component. We define the maximum penalized likelihood (MPL) estimates of the mixture model parameters and then develop a generalized EM (GEM) iterative scheme to compute the MPL estimates. A simulation study and an example from biology are presented.

Original languageEnglish
Pages (from-to)601-612
Number of pages12
JournalStatistics and Computing
Volume21
Issue number4
DOIs
Publication statusPublished - Oct 2011

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