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Activity Number:
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39
- Topics in Clustering
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #328743
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Presentation
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Title:
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Finite Mixture-Of-Gamma Distributions: Estimation, Inference, and Model-Based Clustering
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Author(s):
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Derek S. Young* and Xi Chen and Dilrukshi Hewage and Ricardo N. Poyanco
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Companies:
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University of Kentucky and University of Kentucky and University of Kentucky and FONDAP Center for Genome Regulation
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Keywords:
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cluter analysis;
EM algorithm;
gamma distributions;
mixtools package;
starting values
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Abstract:
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Finite mixtures of (multivariate) Gaussian distributions are one of the most commonly used distributions for 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. The present work contributes to that assertion by developing a general framework for the utility of mixtures of gamma distributions. This talk presents details about maximum likelihood estimation of mixtures of gammas using an expectation-conditional-maximization (ECM). We employ the Wilson-Hilferty normal approximation as part of an effective starting value strategy for our ECM algorithm, as well as part of an effective model-based clustering strategy. We provide some simulation results as well as analyze two real data examples: an infant habituation dataset and a whole genome duplication dataset.
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Authors who are presenting talks have a * after their name.
