Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/141999
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- Title
- Generalized EM estimation for semi-parametric mixture distributions with discretized non-parametric component
- Related
- Statistics and computing, Vol. 21, No. 4, (2011), p.601-612
- DOI
- 10.1007/s11222-010-9195-y
- Publisher
- Springer
- Date
- 2011
- FoR/RFCD Code(s)
-
080200 Computation Theory and Mathematics
010400 Statistics
- Author/Creator
- Ma, Jun
- Author/Creator
- Gudlaugsdottir, Sigurbjorg
- Author/Creator
- Wood, Graham
- Description
- 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 nonparametric 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.
- Description
- 12 page(s)
- Subject Keyword
- 080200 Computation Theory and Mathematics
- Subject Keyword
- 010400 Statistics
- Subject Keyword
- Semi-parametric mixture model
- Subject Keyword
- Maximum penalized likelihood
- Subject Keyword
- Roughness penalty
- Subject Keyword
- Generalized EM
- Resource Type
- journal article
- Organisation
- Macquarie University. Dept. of Statistics
- Identifier
- http://hdl.handle.net/1959.14/141999
- Identifier
- ISSN:1573-1375
- Identifier
- mq-rm-2010003516
- Language
- eng
- Reviewed
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