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Finite Mixture Models

Bibliographic Data

ID23320185
AuthorsGeoffrey J McLachlan (0000-0002-5921-3145, The University of Queensland), Geoffrey McLachlan, Dominic Peel (0000-0001-5281-2041, The University of Queensland), David Peel, Sharon X Lee, Suren I Rathnayake
Year2019
Volume6
Issue1
Pages355-378
Publication date2019-03-07
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnual Review of Statistics and Its Application (JOURNAL)
Journal identifiersISSN: 2326-8298 • E-ISSN: 2326-831X
PublisherAnnual Reviews (PUBLISHER • US)
DOI10.1146/annurev-statistics-031017-100325
OpenAlexW1579271636
Open LibraryOL7598537M
ISBN9780471006268
LanguageEN
Citations received747
References cited127

The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models. Because of their flexibility, mixture models are being increasingly exploited as a convenient, semiparametric way in which to model unknown distributional shapes. This is in addition to their obvious applications where there is group-structure in the data or where the aim is to explore the data for such structure, as in a cluster analysis. It has now been three decades since the publication of the monograph by McLachlan & Basford (1988) with an emphasis on the potential usefulness of mixture models for inference and clustering. Since then, mixture models have attracted the interest of many researchers and have found many new and interesting fields of application. Thus, the literature on mixture models has expanded enormously, and as a consequence, the bibliography here can only provide selected coverage.

Econometrics · Mixture model · Physics · Statistical analysis · Statistical model · Statistical physics · Statistics · Bayesian Methods and Mixture Models · Computer Science · Mathematics · Mixture distributions (Probability theory) · Probabilities

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Unique citing works739
Citations per year25,76
Citation span1997 - 2026 (30)
Citation velocitycurrent
Highly citedYes
Citation typesNeutral: 616

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