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Estimation and Hypothesis Testing in Finite Mixture Models
Murray Aitkin and Donald B. Rubin
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 47, No. 1 (1985), pp. 67-75
Stable URL: http://www.jstor.org/stable/2345545
Page Count: 9
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Finite mixture models are a useful class of models for application to data. When sample sizes are not large and the number of underlying densities is in question, likelihood ratio tests based on joint maximum likelihood estimation of the mixing parameter, λ, and the parameter of the underlying densities, θ, are problematical. Our approach places a prior distribution on λ and estimates θ by maximizing the likelihood of the data given θ with λ integrated out. Advantages of this approach, computational issues using the EM algorithm and directions for further work are discussed. The technique is applied to two examples.
Journal of the Royal Statistical Society. Series B (Methodological) © 1985 Royal Statistical Society