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707,504
Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11972 (17 self)
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situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
Unsupervised learning of finite mixture models
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectationmaximization (EM) alg ..."
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Cited by 418 (22 self)
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This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectationmaximization (EM
5. Finite mixture models
"... Finite mixture models analyses, whether the primary interest of the analysis is the actual clustering of the data or simply the identification of an appropriate model. When a finite mixture model is fitted, one has to decide on the form of the model but also on the number of clusters. It is the latt ..."
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Finite mixture models analyses, whether the primary interest of the analysis is the actual clustering of the data or simply the identification of an appropriate model. When a finite mixture model is fitted, one has to decide on the form of the model but also on the number of clusters
BOOTSTRAPPING FINITE MIXTURE MODELS
 COMPSTAT’2004 SYMPOSIUM
, 2004
"... Finite mixture regression models are used for modelling unobserved heterogeneity in the population. However, depending on the specifications these models need not be identifiable, which is especially of concern if the parameters are interpreted. As bootstrap methods are already used as a diagnostic ..."
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Cited by 8 (6 self)
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Finite mixture regression models are used for modelling unobserved heterogeneity in the population. However, depending on the specifications these models need not be identifiable, which is especially of concern if the parameters are interpreted. As bootstrap methods are already used as a
SOLVING FINITE MIXTURE MODELS IN PARALLEL
, 2003
"... Many economic models are completed by finding a parameter vector θ that optimizes a function f(θ), a task that only be accomplished by iterating from a starting vector θ 0. Use of a generic iterative optimizer to carry out this task can waste enormous amounts of computation when applied to a class o ..."
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of problems defined here as finite mixture models. The finite mixture class is large and important in economics and eliminating wasted computations requires only limited changes to standard code. Further, the approach described here greatly increases gains from parallel execution and opens possibilities
Document Classification Using a Finite Mixture Model
 In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics
, 1997
"... We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM alg ..."
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Cited by 29 (3 self)
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We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM
Assessing Significance in Finite Mixture Models
"... A new method is proposed to quantify significance in finite mixture models. The basis for this new methodology is an approach that calculates the pvalue for testing a simpler model against a more complicated one in a way that is able to obviate the failure of regularity conditions for likelihood ra ..."
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A new method is proposed to quantify significance in finite mixture models. The basis for this new methodology is an approach that calculates the pvalue for testing a simpler model against a more complicated one in a way that is able to obviate the failure of regularity conditions for likelihood
mixtools: An R package for analyzing finite mixture models
 Journal of Statistical Software
, 2009
"... The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture mode ..."
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Cited by 39 (12 self)
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The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture
Constructing Bayesian finite mixture models by the EM algorithm
, 1997
"... In this paper we explore the use of finite mixture models for building decision support systems capable of sound probabilistic inference. Finite mixture models have many appealing properties: they are computationally efficient in the prediction (reasoning) phase, they are universal in the sense that ..."
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Cited by 23 (13 self)
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In this paper we explore the use of finite mixture models for building decision support systems capable of sound probabilistic inference. Finite mixture models have many appealing properties: they are computationally efficient in the prediction (reasoning) phase, they are universal in the sense
The Likelihood Ratio Test for Homogeneity in the Finite Mixture Models
, 2001
"... The authors study the asymptotic behaviour of the likelihood ratio statistic for testing homogeneity in the finite mixture models of a general parametric distribution family. They prove that the limiting distribution of this statistic is the squared supremum of a truncated standard Gaussian process. ..."
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Cited by 50 (6 self)
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The authors study the asymptotic behaviour of the likelihood ratio statistic for testing homogeneity in the finite mixture models of a general parametric distribution family. They prove that the limiting distribution of this statistic is the squared supremum of a truncated standard Gaussian process
Results 1  10
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707,504