This paper develops finite mixture models with fixed effects for two families of distributions for which the incidental parameter problem has a solution. Analytical results are provided for mixtures of Normals and mixtures of Poisson. We provide algorithms based on the expectations-maximization (EM) approach as well as computationally simpler equivalent estimators that can be used in the case of the mixtures of normals. We design and implement a Monte Carlo study that examines the finite sample performance of the proposed estimator and also compares it with other estimators such the Mundlak-Chamberlain conditionally correlated random effects estimator. The results of Monte Carlo experiments suggest that our proposed estimator such models have excellent finite sample properties, even in the case of relatively small  and moderately N sized  dimensions. The methods are applied to models of healthcare expenditures and counts of utilization using data from the Health and Retirement Study. 

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