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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 ..."
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Cited by 11962 (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 leastsquares estimation which is primarily a ..."
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Cited by 111 (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 leastsquares 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 ..."
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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 wellknown 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 popular general method of estimation. Its widespread 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 popular general method of estimation. Its widespread 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
An Example on Maximum Likelihood Estimates
"... In most introdcuctory courses in matlhematical statistics, 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 statistics, 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
Maximum Likelihood Estimation
, 2005
"... Maximum likelihood: counterexamples, examples, and open problems ..."
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