Journal ArticleDOI
A stochastic EM algorithm for mixtures with censored data
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TLDR
This work proposes several solutions to implement the ‘SEMcm algorithm’ (SEM for censored mixture), showing in particular that one of these procedures solves numerical problems arising with the EMcm algorithm and mixtures of nonexponential-type distributions.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 1995-07-01. It has received 65 citations till now. The article focuses on the topics: Mixture model & Expectation–maximization algorithm.read more
Citations
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Journal ArticleDOI
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Journal ArticleDOI
The EM Algorithm—an Old Folk‐song Sung to a Fast New Tune
Xiao-Li Meng,David A. van Dyk +1 more
TL;DR: A general alternating expectation–conditional maximization algorithm AECM is formulated that couples flexible data augmentation schemes with model reduction schemes to achieve efficient computations and shows the potential for a dramatic reduction in computational time with little increase in human effort.
Journal ArticleDOI
A new algorithm for haplotype‐based association analysis: the Stochastic‐EM algorithm
TL;DR: A stochastic version of the EM algorithm, referred to as SEM, could be used for testing haplotype‐phenotype association and provided results similar to those of the NR algorithm, making the SEM algorithm of great interest for haplotypes‐based association analysis, especially when the number of polymorphisms is quite large.
Journal ArticleDOI
Stochastic versions of the em algorithm: an experimental study in the mixture case
TL;DR: It is shown that, for some particular mixture situations, the SEM algorithm is almost always preferable to the EM and “simulated annealing” versions SAEM and MCEM.
ReportDOI
On Stochastic Versions of the EM Algorithm
TL;DR: It is shown that, for some particular mixture situations, the SEM algorithm is almost always preferable to the EM and simulated annealing versions SAEM and MCEM, and the SEM stationary distribution provides a contrasted view of the loglikelihood by emphasizing sensible maxima.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Convergence of Probability Measures
TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
Journal ArticleDOI
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
Journal ArticleDOI
On the convergence properties of the em algorithm
TL;DR: In this paper, the EM algorithm converges to a local maximum or a stationary value of the (incomplete-data) likelihood function under conditions that are applicable to many practical situations.
Journal ArticleDOI
Mixture densities, maximum likelihood, and the EM algorithm
TL;DR: This work discusses the formulation and theoretical and practical properties of the EM algorithm, a specialization to the mixture density context of a general algorithm used to approximate maximum-likelihood estimates for incomplete data problems.