Finite Mixture Models
Abstract
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.
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Parametric bootstrap likelihood-ratio test results were discarded because they had local maxima issues in the bootstrapping process.
“Note that Parametric bootstrap likelihood-ratio test (BLRT) (McLachlan et al., 2000) was also performed, but the test results were discarded because they had local maxima issues in the bootstrapping process.”
From Time in treatment: Examining mental illness trajectories across inpatient psychiatric treatment (2020) · cites this paper as McLachlan et al., 2000
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- Authors
- McLachlan, G., & Peel, D.
- Affiliations
- The University of Queensland (AU)
- ORCID
- McLachlan, Geoffrey
- Venue
- Wiley Series in Probability and Statistics
- Publisher
- Wiley
- Published
- 2000-09-18
- Volume
- 6
- Issue
- 1
- Pages
- 355-378
- ISSN
- 1940-6347 (ISSN-L)
- OpenAlex
- W1579271636
- Cited by
- 10,991
- References
- 1
- Citations by year
- 2026: 541 · 2025: 408 · 2024: 387 · 2023: 370 · 2022: 413 · 2021: 556 · 2020: 535 · 2019: 549 · 2018: 522 · 2017: 480 · 2016: 556 · 2015: 572 · 2014: 608 · 2013: 640 · 2012: 594
- FWCI
- 27.34
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Wiley series in probability and statistics (publishedVersion)
- Annual Review of Statistics and Its Application (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- mixture models, semiparametric models, clustering
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