Results 251 to 260 of about 13,471,124 (303)

Maximum Likelihood Method

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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MAXIMUM LIKELIHOOD AND THE EFFICIENCY OF THE METHOD OF MOMENTS

Biometrika, 1950
One of the practical difficulties in estimation by maximum likelihood arises from the fact that, except in special cases, the resulting equations are complicated and not easily solved. The present paper approaches this problem by deriving an expansion based on moment approximations to the likelihood equations.
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Maximum likelihood and prediction error methods

Automatica, 1979
Abstract 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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Properties of the method of maximum likelihood

1981
In Chapter 3 we drew attention to the need for a general method of point estimation, and the method of maximum likelihood was introduced to fill this role. In this chapter we discuss some properties of the method, and we also discuss some practical problems which arise when using it. The following example illustrates the usefulness of the method.
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The Method of Maximum Likelihood

1999
In the last chapter we introduced the concept of parameter estimation. We have also described the desirable properties of estimators, though without specifying how such estimators can be constructed in a particular case. We have derived estimators only for the important quantities expectation value and variance. We now take on the general problem.
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Maximum-Likelihood-Methode

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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The method of maximum likelihood

1998
Abstract Consider a random variable x distributed according to a p.d.f. f(x; θ). Suppose the functional form of f(x; θ) is known, but the value of at least one parameter θ (or parameters θ = (θ  1, … , θ m)) are not known. That is, f ( x; θ) represents a composite hypothesis for the p.d.f. (cf. Section 4.1).
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Maximum Likelihood Method

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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Maximum-Likelihood Method

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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Pseudo-maximum likelihood method, adjusted pseudo-maximum likelihood method and covariance estimators

Journal of Econometrics, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Broze, Laurence, Gouriéroux, Christian
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