Exact tests of goodness of fit for log-linear models for rates
Exact tests of goodness of fit for log-linear models for rates
We propose Metropolis–Hastings sampling methods for estimating the exact conditional p-value for tests of goodness of fit of log-linear models for mortality rates and standardized mortality ratios. We focus on two-way tables, where the required conditional distribution is a multivariate noncentral hypergeometric distribution with known noncentrality parameter. Two examples are presented: a 2 × 3 table, where the exact results, obtained by enumeration, are available for comparison, and a 9 × 7 table, where Monte Carlo methods provide the only feasible approach for exact inference.
620-624
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
1999
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., Smith, Peter W.F. and Forster, Jonathan J.
(1999)
Exact tests of goodness of fit for log-linear models for rates.
Biometrics, 55 (2), .
(doi:10.1111/j.0006-341X.1999.00620.x).
Abstract
We propose Metropolis–Hastings sampling methods for estimating the exact conditional p-value for tests of goodness of fit of log-linear models for mortality rates and standardized mortality ratios. We focus on two-way tables, where the required conditional distribution is a multivariate noncentral hypergeometric distribution with known noncentrality parameter. Two examples are presented: a 2 × 3 table, where the exact results, obtained by enumeration, are available for comparison, and a 9 × 7 table, where Monte Carlo methods provide the only feasible approach for exact inference.
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Published date: 1999
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Statistics
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Local EPrints ID: 29958
URI: http://eprints.soton.ac.uk/id/eprint/29958
PURE UUID: 14059937-2db3-4163-83a4-6020ca7115ed
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Date deposited: 19 Mar 2007
Last modified: 16 Mar 2024 02:45
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Author:
John W. McDonald
Author:
Jonathan J. Forster
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