Results 271 to 280 of about 376,196 (334)

Maximum Likelihood Estimation and Mathematica

Applied Statistics, 1995
Data and outline Mathematica code are given for several examples of maximum likelihood estimation. A common approach is taken to both elementary complete data problems and more computationally demanding incomplete data problems. In teaching, this common approach brings many conceptually simple but computationally heavy problems within reach of the ...
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A Survey of Maximum Likelihood Estimation

International Statistical Review / Revue Internationale de Statistique, 1972
This survey, which is in two parts, is expository in nature and gives an account of the development of the theory of Maximum Likelihood Estimation (MLE) since its introduction in the papers of Fisher (1922, 1925) up to the present day where original work in this field still continues.
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Maximum likelihood estimators and worst case optimal algorithms for system identification

Systems and Control Letters, 1988
R. Tempo, G. Wasilkowski
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Maximum entropy and maximum likelihood in spectral estimation

IEEE Transactions on Information Theory, 1998
Summary: The power spectral measure, an informative feature of a stationary time-discrete stochastic process, describes the relative strength of uncorrelated frequency components that compose the process. In spectral estimation one wants to describe the spectral measures of processes having a prescribed initial block of autocorrelation coefficients. In
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'Bias reduction of maximum likelihood estimates'

Biometrika, 1993
Summary: It is shown how, in regular parametric problems, the first-order term is removed from the asymptotic bias of maximum likelihood estimates by a suitable modification of the score function. In exponential families with canonical parameterization the effect is to penalize the likelihood by the Jeffreys invariant prior. In binomial logistic models,
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Maximum likelihood estimation

2019
This chapter recalls the basics of the estimation method consisting in maximizing the likelihood associated to the observations. The resulting estimators enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.
Robert A. Rigby, Mikis D. Stasinopoulos, Gillian Z. Heller, Fernanda De Bastiani   +3 more
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Estimating unknown parameters in uncertain differential equation by maximum likelihood estimation

Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2022
Yang Liu, Baoding Liu
semanticscholar   +1 more source

Maximum Likelihood Estimator

Chinese Sociological Review, 2013
Advanced statistical models rely on maximum likelihood (ML) estimators to estimate unknown parameters. Given the complexity and highly technical nature of the numerical approaches embedded in ML, textbooks typically offer oversimplified descriptions of ML, omitting important details from the discussion.
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Maximum likelihood estimators of the parameters of the log-logistic distribution

Statistical Papers, 2018
Xiaofang He, Wangxue Chen, Wen-Bin Qian
semanticscholar   +1 more source

Maximum Likelihood Estimation

1982
This chapter deals with maximum likelihood estimation based on n independent observations X1,...,Xn from the distribution N ⊣ (λ, χ, Ψ).
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