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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Ma, J., Gudlaugsdottir, S. & Wood, G. Generalized EM estimation for semi-parametric mixture distributions with discretized non-parametric component. Stat Comput 21, 601–612 (2011). https://doi.org/10.1007/s11222-010-9195-y
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DOI: https://doi.org/10.1007/s11222-010-9195-y