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May, 1995 The Roles of Conditioning in Inference
N. Reid
Statist. Sci. 10(2): 138-157 (May, 1995). DOI: 10.1214/ss/1177010027

Abstract

This paper reviews the use of sufficient and ancillary statistics in constructing conditional distributions for inference about a parameter. Special emphasis is given to recent developments in accurate approximation of densities, distribution functions and likelihood functions, and to the role of conditioning in these approximations. Exact conditional or marginal inference is available for essentially two classes of models, exponential family models and transformation family models. The approximations are very useful for practical implementation of these exact results. The form of the approximations suggests methods for inference in more general families.

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N. Reid. "The Roles of Conditioning in Inference." Statist. Sci. 10 (2) 138 - 157, May, 1995. https://doi.org/10.1214/ss/1177010027

Information

Published: May, 1995
First available in Project Euclid: 19 April 2007

zbMATH: 0955.62524
MathSciNet: MR1368097
Digital Object Identifier: 10.1214/ss/1177010027

Rights: Copyright © 1995 Institute of Mathematical Statistics

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Vol.10 • No. 2 • May, 1995
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