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Maximum Likelihood Estimation and Mathematica
Applied Statistics, 1995Data 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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On maximum likelihood estimation of a Pareto mixture
Computational Statistics, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bee, Marco +2 more
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A Survey of Maximum Likelihood Estimation
International Statistical Review / Revue Internationale de Statistique, 1972This 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 entropy and maximum likelihood in spectral estimation
IEEE Transactions on Information Theory, 1998Summary: 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, 1993Summary: 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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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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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 Estimation of Misspecified Models
Econometrica, 1982zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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1996
Let \( \{ ({x'_i},{y_i})\} _{i = 1}^N \) be an iid sample drawn from a known distribution F(x i,y i, s), where s is a k × 1 vector of unknown parameters. Let f y|x (y, β) denote the likelihood function of y | x, which is the density function of y | x if y |x is continuous or the probability of y | x if y | x is discrete.
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Let \( \{ ({x'_i},{y_i})\} _{i = 1}^N \) be an iid sample drawn from a known distribution F(x i,y i, s), where s is a k × 1 vector of unknown parameters. Let f y|x (y, β) denote the likelihood function of y | x, which is the density function of y | x if y |x is continuous or the probability of y | x if y | x is discrete.
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A comparison of maximum entropy and maximum likelihood estimation
1997Gegevens betreffende het ondernemerschap op Nederlandse akkerbouwbedrijven zijn in 2 benaderingsmethodes verwerkt, welke onderling op voorspellende nauwkeurigheid en op prijs-elasticiteit zijn ...
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