On asymptotic normality of likelihood and conditional analysis

DOI10.2307/3556179zbMath0498.62019OpenAlexW1972253339MaRDI QIDQ3963853

David J. Brenner, Philip McDunnough, Donald A. S. Fraser

Publication date: 1982

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3556179

zbMATH Keywords

asymptotic normalityconditional inferencenormalized likelihoodconvergence of likelihood functions

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Foundations and philosophical topics in statistics (62A01)

Related Items

The exponential rates of convergence of posterior distributions ⋮ Likelihood decision functions ⋮ Confidence distributions: a review ⋮ Third order asymptotics: connections among test quantities ⋮ An inverse of Sanov's theorem

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• On the Foundations of Statistical Inference
• On Fiducial Inference
• Necessary Analysis and Adaptive Inference
• Likelihood inference about location and scale parameters
• Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
• Two new properties of mathematical likelihood
• The estimation of regression and error-scale parameters, when the joint distribution of the errors is of any continuous form and known apart from a scale parameter
• THE ESTIMATION OF THE LOCATION AND SCALE PARAMETERS OF A CONTINUOUS POPULATION OF ANY GIVEN FORM
• Note on the Consistency of the Maximum Likelihood Estimate