Generalized Neyman-Pearson optimality of empirical likelihood for testing parameter hypotheses

DOI10.1007/S10463-008-0172-6zbMath1332.62068OpenAlexW2087029205MaRDI QIDQ904044

Taisuke Otsu

Publication date: 15 January 2016

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-008-0172-6

zbMATH Keywords

empirical likelihoodgeneralized Neyman-Pearson optimality

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Large deviations (60F10) Asymptotic properties of parametric tests (62F05)

Related Items (1)

On Bahadur efficiency of empirical likelihood

Cites Work

• Unnamed Item
• Unnamed Item
• Large deviation theorems for empirical probability measures
• Empirical likelihood and general estimating equations
• Empirical likelihood is Bartlett-correctable
• Approximation Theorems of Mathematical Statistics
• Empirical likelihood ratio confidence intervals for a single functional
• On universal hypotheses testing via large deviations
• When is the generalized likelihood ratio test optimal?
• On tests for normality
• Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions
• Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators
• Asymptotically Optimal Tests for Multinomial Distributions

This page was built for publication: Generalized Neyman-Pearson optimality of empirical likelihood for testing parameter hypotheses