The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters

James Dickey

Type: Article

Publication Date: 1971-02-01

Citations: 422

DOI: https://doi.org/10.1214/aoms/1177693507

Abstract

Raiffa and Schlaifer's theory of conjugate prior distributions is here applied to Jeffrey's theory of tests for a sharp hypothesis, for simple normal sampling, for model I analysis of variance, and for univariate and multivariate Behrens-Fisher probelms. Leonard J. Savage's Bayesianization of Jeffrey's theory is given with new generalizations. A new conjugate prior family for normal sampling which allows prior independence of unknown mena and variance is given.

Locations

The Annals of Mathematical Statistics - View - PDF

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