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Die Maximum-Likelihood-Methode
2019In Kap. 5 haben wir Schatzer bezuglich gewisser Gutekriterien untersucht. Beispielsweise haben wir gesehen, dass im Kontext des Bernoullimodells die relative Haufigkeit \(\hat{p}\) ein sinnvoller Schatzer fur die Erfolgswahrscheinlichkeit p ist, denn \(\hat{p}\) ist konsistent, erwartungstreu und asymptotisch normalverteilt, vgl. Beispiel 4.4.
Michael Messer, Gaby Schneider
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2016
The maximum-likelihood method offers a possibility to devise estimators of unknown population parameters by circumventing the calculation of expected values like average, variance and higher moments. The likelihood function is defined and its role in formulating the principle of maximum likelihood is elucidated.
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The maximum-likelihood method offers a possibility to devise estimators of unknown population parameters by circumventing the calculation of expected values like average, variance and higher moments. The likelihood function is defined and its role in formulating the principle of maximum likelihood is elucidated.
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1974
In den vorausgegangenen Kapiteln wurden Parameterschatzmethoden behandelt, bei denen keine besonderen Annahmen uber die Verteilungsdichte des Storsignals oder Fehlersignals gemacht werden musten. Die Annahme von Modellen, deren Fehlersignal linear in den Parametern ist, erlaubte dann bei der nichtrekursiven Methode der kleinsten Quadrate eine direkte ...
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In den vorausgegangenen Kapiteln wurden Parameterschatzmethoden behandelt, bei denen keine besonderen Annahmen uber die Verteilungsdichte des Storsignals oder Fehlersignals gemacht werden musten. Die Annahme von Modellen, deren Fehlersignal linear in den Parametern ist, erlaubte dann bei der nichtrekursiven Methode der kleinsten Quadrate eine direkte ...
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1974
In dealing with the problem of estimating the parameters of a structural system of equations, we had not, in previous chapters, explicitly stated the form of the density of the random terms appearing in the system. Indeed, the estimation aspects of classical least squares techniques and their generalization to systems of equations are distribution free,
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In dealing with the problem of estimating the parameters of a structural system of equations, we had not, in previous chapters, explicitly stated the form of the density of the random terms appearing in the system. Indeed, the estimation aspects of classical least squares techniques and their generalization to systems of equations are distribution free,
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Probabilistic Methods: Maximum Likelihood
2015Probabilistic methods for phylogeny aim at ranking trees according to the likelihood of observing the data (i.e. the multiple sequence alignment) given the topology of the tree. In order to compute the probability, the probabilistic tree construction methods estimate P(x |T,t). Here the data is the set of n sequences (taxa), T is the tree and t denotes
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Iterative continuous maximum‐likelihood reconstruction method
Mathematical Methods in the Applied Sciences, 1992AbstractIn this paper we continue our studying of the iterative maximum‐likelihood reconstruction method. We consider only the continuous case and show some convergence properties of the algorithm. In the discrete case convergence has already been proved.
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On the Method of Maximum Likelihood
Journal of the Royal Statistical Society, 1940Not ...
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1994
In dealing with the problem of estimating the parameters of a structural system of equations we had not, in previous chapters, explicitly stated the form of the density of the random terms appearing in the system. Indeed, the estimation aspects of classical least squares techniques and their generalization to systems of equations are distribution free,
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In dealing with the problem of estimating the parameters of a structural system of equations we had not, in previous chapters, explicitly stated the form of the density of the random terms appearing in the system. Indeed, the estimation aspects of classical least squares techniques and their generalization to systems of equations are distribution free,
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Maximum-Likelihood Methods for Phylogeny Estimation
2005Maximum-likelihood (ML) estimation of phylogenies has reached a rather high level of sophistication because of algorithmic advances, improvements in models of sequence evolution, and improvements in statistical approaches and application of cluster computing.
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Simulated Maximum Likelihood, Pseudo‐Maximum Likelihood, and Nonlinear Least Squares Methods
1997AbstractThe simulated analogues to Maximum Likelihood, Pseudo‐Maximum Likelihood, and Non‐Linear Least Squares Methods are presented. Their asymptotic properties and bias corrections are given under various assumptions. Several kinds of simulators are explored and, among them, simulations based on conditioning, on EM algorithm, or on importance ...
Christian Gouriéroux, Alain Monfort
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