Comput Stat Data Anal 51(9):4369–4378Īlmhana J, Liu Z, Choulakian V, McGorman R (2006) A recursive algorithm for gamma mixture models. Akademiai, Budapest, pp 267–281Īl-Saleh JA, Agarwal SK (2007) Finite mixture of gamma distributions: a conjugate prior. In: Petrov BN, Csaki F (eds) Second international symposium on information theory. We provide extensive simulation results that demonstrate the strong performance of our routines as well as analyze two real data examples: an infant habituation dataset and a whole genome duplication dataset.Īkaike H (1973) Information theory and an extension of the maximum likelihood principle. Inference regarding the appropriateness of a common-shape mixture-of-gammas distribution is motivated by theory from research on infant habituation. The Wilson–Hilferty normal approximation is employed as part of an effective starting value strategy for the ECM algorithm, as well as provides insight into an effective model-based clustering strategy. Maximum likelihood estimation of mixtures of gammas is performed using an expectation–conditional–maximization (ECM) algorithm. The present work contributes to that assertion by addressing some facets of estimation and inference for mixtures-of-gamma distributions, including in the context of model-based clustering. There is increasing recognition of mixtures of asymmetric distributions as powerful alternatives to traditional mixtures of Gaussian and mixtures of t distributions. Finite mixtures of (multivariate) Gaussian distributions have broad utility, including their usage for model-based clustering.
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