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Monte Carlo exact tests for log-linear and logistic models.

Monte Carlo exact tests for log-linear and logistic models.
Monte Carlo exact tests for log-linear and logistic models.
The form of the exact conditional distribution of a sufficient statistic for the interest parameters, given a sufficient statistic for the nuisance parameters, is derived for a generalized linear model with canonical link. General results for log-linear and logistic models are given. A Gibbs sampling approach for generating from the conditional distribution is proposed, which enables Monte Carlo exact conditional tests to be performed. Examples include tests for goodness of fit of the all-two-way interaction model for a 28-table and of a simple logistic model. Tests against non-saturated alternatives are also considered.
0035-9246
445-453
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
McDonald, John W.
9adae16e-e1e1-4ddf-bf4c-7231ee8c1c8e
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
McDonald, John W.
9adae16e-e1e1-4ddf-bf4c-7231ee8c1c8e
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Forster, Jonathan J., McDonald, John W. and Smith, Peter W.F. (1996) Monte Carlo exact tests for log-linear and logistic models. Journal of the Royal Statistical Society. Series B: Methodological, 58 (2), 445-453.

Record type: Article

Abstract

The form of the exact conditional distribution of a sufficient statistic for the interest parameters, given a sufficient statistic for the nuisance parameters, is derived for a generalized linear model with canonical link. General results for log-linear and logistic models are given. A Gibbs sampling approach for generating from the conditional distribution is proposed, which enables Monte Carlo exact conditional tests to be performed. Examples include tests for goodness of fit of the all-two-way interaction model for a 28-table and of a simple logistic model. Tests against non-saturated alternatives are also considered.

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Published date: 1996
Organisations: Statistics

Identifiers

Local EPrints ID: 29952
URI: https://eprints.soton.ac.uk/id/eprint/29952
ISSN: 0035-9246
PURE UUID: b0c28af6-42e7-47db-beb6-110894889aeb
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

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Date deposited: 19 Mar 2007
Last modified: 06 Jun 2018 13:11

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Contributors

Author: John W. McDonald

University divisions

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