Generalized EM estimation for semi-parametric mixture distributions with discretized non-parametric component |
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Authors: | Jun Ma Sigurbjorg Gudlaugsdottir Graham Wood |
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Institution: | 1.Department of Statistics,Macquarie University,Sydney,Australia |
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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. |
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Keywords: | |
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