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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
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Cited by 11972 (17 self)
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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
Tutorial on maximum likelihood estimation.
- Journal of Mathematical Psychology,
, 2003
"... Abstract In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a ..."
Abstract
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Cited by 115 (3 self)
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Abstract In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a
Robust Maximum Likelihood Estimation
"... In this work we examine the problem of maximum likelihood estimation of the parameters of a Gaussian distribution in the cases where the observed data is either very noisy or very sparse. We use the robust optimization paradigm to address this problem. ..."
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In this work we examine the problem of maximum likelihood estimation of the parameters of a Gaussian distribution in the cases where the observed data is either very noisy or very sparse. We use the robust optimization paradigm to address this problem.
Symbolic maximum likelihood estimation with
- MATHEMATICA, J. Roy. Stat. Soc. Series D
, 2000
"... Mathematica is a symbolic programming language that empowers the user to undertake complicated algebraic tasks. One such task is the derivation of maximum likelihood estimators, demonstrably an important topic in statistics at both the research and expository level. In this paper, a Mathematica pack ..."
Abstract
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Cited by 7 (0 self)
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Mathematica is a symbolic programming language that empowers the user to undertake complicated algebraic tasks. One such task is the derivation of maximum likelihood estimators, demonstrably an important topic in statistics at both the research and expository level. In this paper, a Mathematica
AND MAXIMUM LIKELIHOOD ESTIMATES
, 1991
"... Functions called generalized means are of interest in statistics because they are simple to compute, have intuitive appeal, and can serve as reasonable parameter estimates. arithmetic, geometric, and harmonic means are all examples of generalized means. The well-known We show how generalized means c ..."
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can be derived in a unified way, as least squares estimates for a transformed data set. We also investigate models that have generalized means as their maximum likelihood estimates.
MAXIMUM LIKELIHOOD ESTIMATION
"... Maximum likelihood is by far the most pop-ular general method of estimation. Its wide-spread acceptance is seen on the one hand in the very large body of research dealing with its theoretical properties, and on the other in the almost unlimited list of applications. To give a reasonably general defi ..."
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Maximum likelihood is by far the most pop-ular general method of estimation. Its wide-spread acceptance is seen on the one hand in the very large body of research dealing with its theoretical properties, and on the other in the almost unlimited list of applications. To give a reasonably general
Maximum Likelihood Estimation
, 2008
"... Estimation Methods Estimation of parameters is a fundamental problem in data analysis. This paper is about maximum likelihood estimation, which is a method that finds the most likely value for the parameter based on the data set collected. A handful of estimation methods existed before maximum like ..."
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Cited by 1 (0 self)
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Estimation Methods Estimation of parameters is a fundamental problem in data analysis. This paper is about maximum likelihood estimation, which is a method that finds the most likely value for the parameter based on the data set collected. A handful of estimation methods existed before maximum
An Example on Maximum Likelihood Estimates
"... In most introdcuctory courses in matlhematical sta-tistics, students see examples and work problems in which the maximum likelihood estimate (MLE) of a parameter turns out to be either the sample meani, the sample variance, or the largest, or the smallest sample item. The purpose of this note is to ..."
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In most introdcuctory courses in matlhematical sta-tistics, students see examples and work problems in which the maximum likelihood estimate (MLE) of a parameter turns out to be either the sample meani, the sample variance, or the largest, or the smallest sample item. The purpose of this note
Results 1 - 10
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