Results 221 to 230 of about 229,863 (257)

A note on a partial empirical likelihood

Biometrika, 2002
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Zou, F., Fine, J. P.
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A Review of Empirical Likelihood

Annual Review of Statistics and Its Application, 2021
Empirical likelihood is a popular nonparametric analog of the usual parametric likelihood, inheriting many of the large-sample properties of the latter construct. This article presents a review of the empirical likelihood approach from its introduction 30 years ago, up to recent theoretical developments.
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Self‐concordance for empirical likelihood

Canadian Journal of Statistics, 2013
AbstractAbstractThe usual approach to computing empirical likelihood for the mean uses Newton's method after eliminating a Lagrange multiplier and replacing the function by a quadratic Taylor approximation to the left of . This paper replaces the quadratic approximation by a quartic.
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Data Squashing by Empirical Likelihood

Data Mining and Knowledge Discovery, 2003
Data squashing was introduced by W. DuMouchel, C. Volinsky, T. Johnson, C. Cortes, and D. Pregibon, in Proceedings of the 5th International Conference on KDD (1999). The idea is to scale data sets down to smaller representative samples instead of scaling up algorithms to very large data sets.
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Empirical Likelihood Methods

2020
Publisher Summary Likelihood-based estimation methods in survey sampling do not follow as special cases from classical parametric likelihood inferences. The only randomization is induced by the probability sampling selection of units. While intervals based on NAs are clearly inappropriate, the EL interval maintains the same desirable performance ...
Changbao Wu, Mary E. Thompson
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Empirical Likelihood

Journal of the American Statistical Association, 2002
Zhao Y., Shen X.
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Empirical Likelihood with Censored Data

2023
Mohamed Boukeloua, Amor Keziou
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A review of recent advances in empirical likelihood

Wiley Interdisciplinary Reviews: Computational Statistics, 2023
Yichuan Zhao
exaly  

Empirical Likelihood with Applications

2017
The maximum likelihood method for regular parametric models has many optimality properties. As a result, it is one of the most popular methods in statistical inference. However, model mis-specification is a big concern since a misspecified model may lead to bias results.
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On empirical composite likelihoods

2010
Composite likelihood functions are convenient surrogates for the ordinary likelihood, when the latter is too difficult or even impractical to compute, and they may be more robust to model misspecication. One drawback of composite likelihood methods is that the composite likelihood analogue of the likelihood ratio statistic does not have the standard 2 ...
LUNARDON, NICOLA, PAULI, FRANCESCO, Ventura L.   +2 more
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