Results 221 to 230 of about 1,853,165 (304)
Correcting for publication bias in a meta-analysis with the p-uniform* method. [PDF]
Psychon Bull Revvan Aert RCM, van Assen MALM.
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Efficient Estimation Methods for the QR Distribution with Type-II Censored Data: An Empirical Validation on Lung Cancer Prognosis. [PDF]
Entropy (Basel)Ramzan Q, Amin M, Alghamdi S, Alharbi R.
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Estimating Baseline Survival Function in the Proportional Hazards Model Under Monotone Hazards. [PDF]
J Stat Theory PractWu Y, Chappell R.
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SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT. [PDF]
Ann Appl StatDeng Y, Zeng D, Wang Y.
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On the maximum likelihood method in molecular phylogenetics
Journal of Molecular Evolution, 1991The efficiency of obtaining the correct tree by the maximum likelihood method (Felsenstein 1981) for inferring trees from DNA sequence data was compared with trees obtained by distance methods. It was shown that the maximum likelihood method is superior to distance methods in the efficiency particularly when the evolutionary rate differs among lineages.
Masami Hasegawa +2 more
exaly +4 more sources
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
semanticscholar +3 more sources
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
semanticscholar +3 more sources
On the Maximum Likelihood Method of Identification
IBM Journal of Research and Development, 1970The maximum likelihood principle of estimation applied to the linear black-box identification problem gives models with theoretically attractive properties. Also, the method has been applied to industrial data (various processes in paper production) and proved able to work in practice.This paper presents further developments of the method in the case ...
T. Bohlin
openaire +3 more sources
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.
S. Širca
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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.
S. Širca
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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
A. Ziegler
semanticscholar +3 more sources
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
A. Ziegler
semanticscholar +3 more sources
Spatial Statistics, 2018
This paper investigates variable selection in the spatial autoregressive model with independent and identical distributed errors. A penalized quasi-maximum likelihood method is developed for simultaneous model selection and parameter estimation.
Xuan Liu, Jianbao Chen, Suli Cheng
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This paper investigates variable selection in the spatial autoregressive model with independent and identical distributed errors. A penalized quasi-maximum likelihood method is developed for simultaneous model selection and parameter estimation.
Xuan Liu, Jianbao Chen, Suli Cheng
semanticscholar +1 more source