Results 261 to 270 of about 259,116 (299)

Robust Empirical Likelihood

2021
In this paper, we present a robust version of the empirical likelihood estimator for semiparametric moment condition models. This estimator is obtained by minimizing the modified Kullback-Leibler divergence, in its dual form, using truncated orthogonality functions. Some asymptotic properties regarding the limit laws of the estimators are stated.
Amor Keziou, Aida Toma
openaire   +1 more source

Bayesian empirical likelihood

Biometrika, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

A note on a partial empirical likelihood

Biometrika, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zou, F., Fine, J. P.
openaire   +1 more source

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.
openaire   +1 more source

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.
openaire   +1 more source

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.
openaire   +1 more source

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
openaire   +1 more source

Marginal likelihood, conditional likelihood and empirical likelihood: Connections and applications

Biometrika, 2005
Marginal likelihood and conditional likelihood are often used for eliminating nuisance parameters. For a parametric model, it is well known that the full likelihood can be decomposed into the product of a conditional likelihood and a marginal likelihood. This property is less transparent in a nonparametric or semiparametric likelihood setting.
Jing Qin, Biao Zhang
openaire   +1 more source

Empirical Likelihood

Journal of the American Statistical Association, 2002
Zhao Y., Shen X.
openaire   +2 more sources

Empirical Likelihood with Censored Data

2023
Mohamed Boukeloua, Amor Keziou
openaire   +1 more source