Results 261 to 270 of about 3,652,623 (306)

Assigning a Likelihood Function

2020
As scientists, we want to know how to parameterise our models, make comparisons with other models, and quantify model predictive uncertainty. For all these purposes, measurement data are needed, but how exactly should we use the data? The answer is always the same: in the likelihood function.
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A projected likelihood function for semiparametric models

Biometrika, 1992
Summary: In a sequence of papers, the first author [Can J. Stat. 12, 265-282 (1984; Zbl 0574.62084)], \textit{J. E. Hutton} and \textit{P. I. Nelson} [Stochastic Processes Appl. 22, 245-257 (1986; Zbl 0616.62113)], and \textit{V. P. Godambe} and \textit{C. C. Heyde} [Int. Stat. Rev.
McLeish, D. L., Small, Christopher G.
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The Plausibility and Likelihood Functions

2003
The notion of likelihood is an important concept in modern statistics. In particular, the likelihood ratio has been used by several authors [19, 37] to measure the strength of the evidence represented by observations in statistical problems. This idea works fine when the goal is to evaluate the strength of the available evidence for a simple hypothesis
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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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On the Empirical Likelihood Ratio for Smooth Functions of M‐functionals

Scandinavian Journal of Statistics, 1997
It is known that the empirical likelihood ratio can be used to construct confidence regions for smooth functions of the mean, Fréchet differentiable statistical functionals and for a class of M‐functionals. In this paper, we argue that this use can be extended to the class of functionals which are smooth functions of M‐functionals.
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A deviance function for the quasi-likelihood method

Biometrika, 1993
Summary: We introduce a deviance function that can be used in conjunction with the quasi-likelihood method. The need for such functions arises when the quasi-log likelihood function is not uniquely defined. The deviance is obtained by projecting a pair of centered likelihood ratios onto the direct sum of two Hilbert spaces spanned by the observations ...
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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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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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Constrained likelihood function for a uniform array

IEEE Transactions on Signal Processing, 1994
The symmetry of a uniform linear array can be exploited to construct a simplified analytical representation for the maximum likelihood function. This results in a significant decrease in the computational load and allows the algorithm to be used with large arrays. >
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The two source maximum likelihood function

IEEE Signal Processing Letters, 1994
The maximum likelihood method is designed to yield high resolution estimates in a multiple source environment. The article derive a simplified representation of the maximum likelihood function for the two source case. This case is instructive to understand when and why the full power of the maximum likelihood method should be used.
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