Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering

Derek S. Young (), Xi Chen, Dilrukshi C. Hewage and Ricardo Nilo-Poyanco
Additional contact information
Derek S. Young: University of Kentucky
Xi Chen: University of Kentucky
Dilrukshi C. Hewage: University of Kentucky
Ricardo Nilo-Poyanco: Universidad Mayor

Advances in Data Analysis and Classification, 2019, vol. 13, issue 4, No 10, 1053-1082

Abstract: Abstract Finite mixtures of (multivariate) Gaussian distributions have broad utility, including their usage 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 addressing some facets of estimation and inference for mixtures-of-gamma distributions, including in the context of model-based clustering. Maximum likelihood estimation of mixtures of gammas is performed using an expectation–conditional–maximization (ECM) algorithm. 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. Inference regarding the appropriateness of a common-shape mixture-of-gammas distribution is motivated by theory from research on infant habituation. 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.

Keywords: ECM algorithms; Finite mixture models; Identifiability; Mixturegram; Multivariate Gaussian copula; Starting values; 62F03; 62H30; 62P15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11634-019-00361-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:13:y:2019:i:4:d:10.1007_s11634-019-00361-y

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-019-00361-y

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().