Minumum Hellinger distance estimation for Poisson mixtures
Computational Statistics & Data Analysis
Robust estimation of mixture complexity for count data
Computational Statistics & Data Analysis
Three approaches to probability model selection
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
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For count data, robust estimation of the number of mixture components in finite mixtures is revisited using L"2 distance. An information criterion based on L"2 distance is shown to yield an estimator, which is also shown to be strongly consistent. Monte Carlo simulations show that our estimator is competitive with other procedures in correctly determining the number of components when the data comes from Poisson mixtures. When the data comes from a negative binomial mixture but the postulated model is a Poisson mixture, simulations show that our estimator is highly competitive with the minimum Hellinger distance (MHD) estimator in terms of robustness against model misspecification. Furthermore, we illustrate the performance of our estimator for a real dataset with overdispersion and zero-inflation. Computational simplicity combined with robustness property makes the L"2E approach an attractive alternative to other procedures in the literature.