LIMITING PROPERTIES OF LIKELIHOODS AND INFERENCE.

Abstract

The paper supports to some extent the idea that the likelihood principle is applicable within a wide area. The sphere of applicability does not depend on the form of the sample space but on the form of likelihood functions (likelihoods, for short). If the likelihoods are exactly normal, then being given a likelihood function we may draw inference ignoring the statistical model in the background. The main purpose of the paper is to investigate how far the background model may be ignored, if the likelihoods are only approximately normal. The investigation will be done in the framework of asymptotic theory, whose basic features the paper discusses. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1970
Accession Number
AD0712028

Entities

People

  • Jaroslav Hajek

Organizations

  • Florida State University

Tags

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Plasma Physics / Magnetohydrodynamics

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval
  • Space