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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 11807 (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
Robust Filter Design based on Generalized MaximumLikelihood Estimation
"... Abstract: Maximumlikelihood estimation is a commonly used technique in digital signal processing. Unfortunately most digital filter algorithms show a lack of robustness in impulsive environments, which is a result of heavy tails in the corresponding noise distributions as explained in [1,2]. In thi ..."
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,2]. In this paper, a novel method for robust filter design is presented which is based on the generalized maximumlikelihood estimation (MEstimation) originating from the mathematical field of robust statistics. In comparison to the well known maximumlikelihood technique the novel method permits the usage of a
Generalized Maximum Likelihood Estimates for Infinite Dimensional Exponential Families
, 2006
"... The notion of generalized maximum likelihood estimate for finite dimensional canonically convex exponential families, studied in detail in previous works of the authors, is extended to an infinite dimensional setting. Existence of the estimate when a generalized loglikelihood function is bounded ab ..."
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Cited by 1 (0 self)
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The notion of generalized maximum likelihood estimate for finite dimensional canonically convex exponential families, studied in detail in previous works of the authors, is extended to an infinite dimensional setting. Existence of the estimate when a generalized loglikelihood function is bounded
Selfinformative Limits of Bayes Estimates and Generalized Maximum Likelihood
, 2003
"... A definition of selnformative Bayes carriers or limits is given as a description of an approach to noninformative Bayes estimation in non and semiparametric models. It takes the posterior w.r.t. a prior as a new prior and repeats this procedure again and again. A main objective of the paper is to ..."
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is to clarify the relation between selfinformative carriers or limits and maximum likelihood estimates (MLE's). For a model with dominated probability distributions we state sucient conditions under which the set of MLE's is a selnformative carrier or in the case of a unique MLE its selnformative
On Identifiablity in Weighted Distributions Using Generalized Maximum Likelihood Estimation
"... Abstract. In this research, the generalized maximum likelihood estimator (GMLE) is used to investigate the parameters estimation for weighted distributions. There exist situations where the random sample from the population of interest is not available due to the data having unequal probabilities ..."
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Abstract. In this research, the generalized maximum likelihood estimator (GMLE) is used to investigate the parameters estimation for weighted distributions. There exist situations where the random sample from the population of interest is not available due to the data having unequal probabilities
GENERAL MAXIMUM LIKELIHOOD EMPIRICAL BAYES ESTIMATION OF NORMAL MEANS
, 908
"... We propose a general maximum likelihood empirical Bayes (GMLEB) method for the estimation of a mean vector based on observations with i.i.d. normal errors. We prove that under mild moment conditions on the unknown means, the average mean squared error (MSE) of the GMLEB is within an infinitesimal f ..."
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Cited by 26 (1 self)
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We propose a general maximum likelihood empirical Bayes (GMLEB) method for the estimation of a mean vector based on observations with i.i.d. normal errors. We prove that under mild moment conditions on the unknown means, the average mean squared error (MSE) of the GMLEB is within an infinitesimal
Generalized MaximumLikelihood Sequence Detection for PhotonCounting Free Space Optical Systems
"... Abstract—We investigate detection methods for onoff keying (OOK) photoncounting Free Space Optical (FSO) systems in the presence of turbulenceinduced fading, assuming no channel state information at the receiver. To recover the performance loss which is associated with symbolbysymbol detection ..."
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lowcomplexity detection rule, which is based on the generalized maximumlikelihood sequence estimation. The proposed scheme allows the detection of sequence lengths that are prohibitive for conventional MLSD, without using any kind of channel knowledge. Monte Carlo simulation results show its
Generalized Maximum Likelihood Method for Ultrahigh Energy Cosmic Rays
, 810
"... Abstract. The Maximum Likelihood Method is generalized to include effects ..."
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Abstract. The Maximum Likelihood Method is generalized to include effects
Smoothers and the C p, Generalized Maximum Likelihood, and Extended Exponential Criteria: A Geometric Approach
"... Nonparametric regression, often called smoothing, is a widely used data analysis method. The use of a smoother requires the choice of a smoothing parameter that by balancing � delity and roughness controls how much smoothing is done. Two popular selection criteria for choosing the smoothing paramete ..."
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parameter are Cp and generalized maximum likelihood (GML). Each of these has its own problems. For Cp, the problem is its high variability, whereas for GML, the problem is its potentially large bias. By studying the geometry of selection criteria, we give an intuitive explanation of the strength
Results 1  10
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530,917