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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 ..."
Abstract

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
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
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
Identifiability of finite mixtures of elliptical distributions
"... We present general results on the identifiability of finite mixtures of elliptical distributions under conditions on the characteristic generators or density generators. Examples include the multivariate t distribution, symmetric stable laws, exponential power and Kotz distributions. In each case, ..."
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Cited by 9 (1 self)
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We present general results on the identifiability of finite mixtures of elliptical distributions under conditions on the characteristic generators or density generators. Examples include the multivariate t distribution, symmetric stable laws, exponential power and Kotz distributions. In each case
Nonparametric estimation of finite mixtures
, 2013
"... Abstract. The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models from data with repeated measurements. Three measurements suffice for the mixture to be fully identified and so our approach can be used even with very short panel data. We provide distribution ..."
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Cited by 4 (2 self)
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Abstract. The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models from data with repeated measurements. Three measurements suffice for the mixture to be fully identified and so our approach can be used even with very short panel data. We provide
Finite Mixture EFA in Mplus
, 2007
"... In this document we describe the Mixture EFA model estimated in Mplus. Four types of dependent variables are possible in this model: normally distributed, ordered categorical with logit or probit link, Poisson distributed with the exponential link function, and censored variables. Inflation is not a ..."
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In this document we describe the Mixture EFA model estimated in Mplus. Four types of dependent variables are possible in this model: normally distributed, ordered categorical with logit or probit link, Poisson distributed with the exponential link function, and censored variables. Inflation
Applications of finite mixtures of regression models
"... Package flexmix provides functionality for fitting finite mixtures of regression models. The available model class includes generalized linear models with varying and fixed effects for the component specific models and multinomial logit models for the concomitant variable models. This model class in ..."
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Package flexmix provides functionality for fitting finite mixtures of regression models. The available model class includes generalized linear models with varying and fixed effects for the component specific models and multinomial logit models for the concomitant variable models. This model class
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
Using the EM Algorithm for Finite Mixtures
"... This paper presents a detailed description of maximum parameter estimation for item response models using the general EM algorithm. In this paper the models are specified using a univariate discrete latent ability variable. When the latent ability variable is discrete the distribution of the observe ..."
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of the observed item responses is a finite mixture, and the EM algorithm for finite mixtures can be used. Maximum likelihood estimates of the item parameters and of the discrete probabilities of the latent ability distribution are given using the EM algorithm for finite mixtures. Results are presented in general
Results 1  10
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