Maximum Likelihood in a Generalized Linear Finite Mixture Model by Using the EM Algorithm

A generalized linear finite mixture model and an EM algorithm to fit the model to data are described. By this approach the finite mixture model is embedded within the general framework of generalized linear models (GLMs). Implementation of the proposed EM algorithm can be readily done in statistical packages with facilities for GLMs. A practical example is presented where a generalized linear finite mixture model of ten Weibull distributions is adopted. The example is concerned with the flow cytometric measurement of the DNA content of spermatids in a mutant mouse, which shows non-disjunction of specific chromosomes during meiosis.

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