Results 311 to 320 of about 13,556,907 (351)

Information from the maximized likelihood function

Biometrika, 1985
Suppose x = (x1, ..., xj) is a random sample of either scalar or vector observations from a density f(x, c(), where w( E Q is partitioned into a set 0 = (01, ..., Or) of parameters of direct interest and 4 = (4 1, ..., 4,q) of nuisance parameters.
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On empirical likelihood statistical functions

Journal of Econometrics, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zheng, G, Yuan, A, Xu, J
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Estimating Functions and Approximate Conditional Likelihood

Biometrika, 1987
The approximate conditional likelihood method proposed by \textit{D. R. Cox} and \textit{N. Reid}, J. R. Stat. Soc., Ser. B 49, 1-39 (1987; Zbl 0616.62006) is applied to the estimation of a scalar parameter \(\theta\), in the presence of nuisance parameters.
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STATISTICAL INFERENCE WITH SIMULATED LIKELIHOOD FUNCTIONS

Econometric Theory, 1999
Summary: This paper considers classical test statistics, namely, the likelihood ratio, efficient score, and Wald statistics, for econometric models under simulation estimation. The simulated likelihood ratio, simulated efficient score, and simulated Wald test statistics are shown to be asymptotically equivalent.
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Inferential estimation, likelihood, and maximum likelihood linear estimation functions

1991
Abstract The purpose of’inferential’ estimation, as contrasted with ‘point’ estimation, is to make estimation statements. An estimation statement is a quantitative statement of uncertainty about μ using all the information supplied by X.
S R Chamberlin, D A Sprott
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Factoring the likelihood function

1996
Abstract The likelihood function provides an overall assessment of the relative merits of different members of a given family of statistical models, although this must be balanced against their relative complexity. However, as we saw in Section 3.6.3, we often require measures of precision of the estimates of individual parameters in the
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Entropies of Likelihood Functions

1992
We show that the normalized likelihood function needed to get from a prior probability vector to the posterior that results from the minimum cross-entropy inference process has the highest entropy among all probability vectors satisfying an appropriate set of linear constraints. We regard the domains of the entropy and cross-entropy functions as groups.
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Maximum Likelihood Transfer Function Modelling

1992
A state-space realization of the transfer-function model of Box and Jenkins (1976) is used to compute the exact Gaussian likelihood of the stationary input-output series, (X t’ Y t),t = 1,…,n, and to compute the exact linear mean-square predictor of the output Y t+h based on (X t’ Y t),t = 1,…,n. We show how to use the state-space formulation for model
P. J. Brockwell, R. A. Davis, H. Salehi
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EMPIRICAL LIKELIHOOD AND DIFFERENTIABLE FUNCTIONALS

2016
Empirical likelihood (EL) is a recently developed nonparametric method of statistical inference. It has been shown by Owen (1988,1990) and many others that empirical likelihood ratio (ELR) method can be used to produce nice confidence intervals or regions. Owen (1988) shows that -2logELR converges to a chi-square distribution with one degree of freedom
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Refinement of macromolecular structures by the maximum-likelihood method.

Acta Crystallographica Section D: Biological Crystallography, 1997
G. Murshudov, A. Vagin, E. Dodson
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