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Maximum-likelihood estimation of the entropy of an attractor
Physical Review E, 1994In this paper, a maximum-likelihood estimate of the (Kolmogorov) entropy of an attractor is proposed that can be obtained directly from a time series. Also, the relative standard deviation of the entropy estimate is derived; it is dependent on the entropy and on the number of samples used in the estimation.
Schouten, J.C. (author) +2 more
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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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A Note on a Maximum-Likelihood Estimate
Econometrica, 1947An estimate of y obtained by applying the method of maximum likelihood under the assumption that ut is normally distributed is consistent and asymptotically normally distributed. The asymptotic standard deviation is given in this note. Although Kendall considers many estimates of the period in his publication, he does not use the maximum-likelihood ...
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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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Maximum Likelihood Estimation of Misspecified Models
Econometrica, 1982zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the Maximum Likelihood Estimator of the Emax Model
It is known in the literature that maximum likelihood esti- mation of the Emax model parameters often encounters computational problems. Our contribution provides a new understanding and control of all the experimental situations. In particular, exact MLE for a three-point experimental design is shown, and we identify the two scenarios where the MLE ...G. Aletti, C. May, C. Tommasi
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1971
It is possible to develop a number of systems of estimation and nowhere does this seem to be more true than for the estimation of genetic crossover fractions. Several of these fail dismally because of inaccuracy and inefficiency (Fisher and Balmukand, 1928).
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It is possible to develop a number of systems of estimation and nowhere does this seem to be more true than for the estimation of genetic crossover fractions. Several of these fail dismally because of inaccuracy and inefficiency (Fisher and Balmukand, 1928).
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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.
openaire +1 more source