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2013
A classical problem in the statistical decision theory is to estimate the probability distribution of a random vector X given its independent observations \(x_{1},\ldots,x_{n}\). Often it is assumed that the probability distribution comes from some family of functions parametrized by a set of parameters \(\theta _{1},\ldots,\theta _{m}\), so that in ...
Michael Zabarankin, Stan Uryasev
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A classical problem in the statistical decision theory is to estimate the probability distribution of a random vector X given its independent observations \(x_{1},\ldots,x_{n}\). Often it is assumed that the probability distribution comes from some family of functions parametrized by a set of parameters \(\theta _{1},\ldots,\theta _{m}\), so that in ...
Michael Zabarankin, Stan Uryasev
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2011
The most popular estimation approach is the maximum likelihood (ML) method. In this chapter, the ML estimator is defined first, and important asymptotic properties of the ML estimator are formulated in Sect. 4.2. Trans- formations of estimators, not only ML estimators, are discussed in Sect. 4.3. To illustrate the ML approach, we consider the ML method
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The most popular estimation approach is the maximum likelihood (ML) method. In this chapter, the ML estimator is defined first, and important asymptotic properties of the ML estimator are formulated in Sect. 4.2. Trans- formations of estimators, not only ML estimators, are discussed in Sect. 4.3. To illustrate the ML approach, we consider the ML method
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2006
Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
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Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
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Maximum likelihood and prediction error methods
Automatica, 1979Abstract The basic ideas behind the parameter estimation methods are discussed in a general setting. The application to estimation or parameters in dynamical systems is treated in detail using the prototype problem of estimating parameters in a continuous time system using discrete time measurements. Computational aspects are discussed.
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Journal of Econometrics, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Broze, Laurence, Gouriéroux, Christian
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Broze, Laurence, Gouriéroux, Christian
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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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