Finite Mixture Models

By P. Deb. 2008

5,195 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

Citations in the corpus

An Introduction to Probabilistic Programming (2018, methodology): “We will begin with a three-component Gaussian mixture model (McLachlan and Peel, 2004).”
Joint Unsupervised Learning of Deep Representations and Image Clusters (2016, background): “Related ideas form the basis of a number of methods, such as expectation maximization (EM) [7, 37], spectral clustering [40, 47, 61], and non-negative matrix factorization (NMF) based clustering [1, 8, 60].”
Identifiability of parameters in latent structure models with many observed variables (2008, background): “Latent structure models form a very large class of models including, for instance, finite univariate or multivariate mixtures [34], hidden Markov models [5, 16] and nonparametric mixtures [33].”
Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. (2020, background): ““LCA” is the label given to a form of finite mixture modeling where the observed indicators are all categorical (21).”
Learning Models with Uniform Performance via Distributionally Robust Optimization (2018, background): “When one believes there may be latent subpopulations, mixture model approaches can model latent membership directly [3, 40, 66, 26].”
On false discovery control under dependence (2008, methodology): “A popular framework for the false discovery control is the random effects model or the two-component mixture model (McLachlan and Peel [12]) in which the null hypotheses Hi,1 ≤ i ≤ n, are assumed to be independent Bernoulli random variables.”
Estimation of a two‐component mixture model with applications to multiple testing (2012, background): “. In contamination problems the distribution Fb, for which reasonable assumptions can be made, maybe contaminated by an arbitrary distribution Fs yielding a sample drawn from F as in (1.1); see e.g., [MP00]. For example, in astronomy, such situations arise quite often: when observing some variable(s”
General Growth Mixture Analysis of Adolescents' Developmental Trajectories of Anxiety: The Impact of Untested Invariance Assumptions on Substantive Interpretations (2011, methodology): “They are (a) the Consistent Akaike Information Criterion (CAIC; Bozdogan, 1987), (b) the Bayesian Information Criterion (BIC; Schwartz, 1978), (c) the sample-size adjusted BIC (ABIC; Sclove, 1987), and (d) the Bootstrap Likelihood Ratio Test (BLRT; McLachlan & Peel, 2000).”
Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data (2016, positive): “The problem is often addressed using the expectation-maximization (EM) algorithm which considers the so-called incomplete problem [19].”
Non‐parametric estimation of finite mixtures from repeated measurements (2016, background): “The possibility of relabelling is an ambiguity that is inherent to mixtures (see McLachlan and Peel (2000), section 4.9), and we shall henceforth leave it implicit.”
Profiling retirees in the retirement transition and adjustment process: examining the longitudinal change patterns of retirees' psychological well-being. (2007, background): “The selection criteria for the optimal growth mixture model have been discussed extensively in the statistical literature on finite mixture modeling (for a review, see McLachlan & Peel, 2000).”
Finite mixture modelling using the skew normal distribution (2007, methodology): “Finite mixture models have been broadly developed and widely applied to classification, clustering, density estimation and pattern recognition problems, as shown by Titterington, Smith and Markov (1985), McLachlan and Basord (1988), McLachlan and Peel (2000), and the references therein.”
Robust continuous clustering (2017, background): “Signiﬁcant advances in the last two decades include spectral clustering (2–4), generalizations of classic center-based methods (5, 6), mixture models (7, 8), mean shift (9), afﬁnity propagation (10), subspace clustering (11–13), nonparametric methods (14, 15), and feature selection (16–20).”
The construct of psychophysiological reactivity: Statistical and psychometric issues (2013, background): “However, this is also an area where modern latent variable methods, such as latent class analysis (McCutcheon, 1987) and finite mixture modeling (McLachlan & Peel, 2000) can offer advantages over earlier-developed methods, by accounting for error in classification and within-category variability.”
Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment (2013, methodology): “These statistical models are appropriate when one posits that a population is comprised of two or more underlying, latent subgroups defined by the intersection of numerous individual characteristics (Everitt and Hand 1981; McLachlan and Peel 2000; Titterington et al. 1985).”