Exact Tests via Complete Enumeration: A Distributed Computing Approach
Exact Tests via Complete Enumeration: A Distributed Computing Approach
The analysis of categorical data often leads to the analysis of a contingency table. For large samples, asymptotic approximations are sufficient when calculating p-values, but for small samples the tests can be unreliable. In these situations an exact test should be considered. This bases the test on the exact distribution of the test statistic. Sampling techniques can be used to estimate the distribution. Alternatively, the distribution can be found by complete enumeration. A new algorithm is developed that enables a model to be defined by a model matrix, and all tables that satisfy the model are found. This provides a more efficient enumeration mechanism for complex models and extends the range of models that can be tested. The technique can lead to large calculations and a distributed version of the algorithm is developed that enables a number of machines to work efficiently on the same problem.
Michaelides, Danius T.
a6df5175-d71a-4cd4-befc-26c48235fb92
1997
Michaelides, Danius T.
a6df5175-d71a-4cd4-befc-26c48235fb92
Michaelides, Danius T.
(1997)
Exact Tests via Complete Enumeration: A Distributed Computing Approach.
University of Southampton, : University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
The analysis of categorical data often leads to the analysis of a contingency table. For large samples, asymptotic approximations are sufficient when calculating p-values, but for small samples the tests can be unreliable. In these situations an exact test should be considered. This bases the test on the exact distribution of the test statistic. Sampling techniques can be used to estimate the distribution. Alternatively, the distribution can be found by complete enumeration. A new algorithm is developed that enables a model to be defined by a model matrix, and all tables that satisfy the model are found. This provides a more efficient enumeration mechanism for complex models and extends the range of models that can be tested. The technique can lead to large calculations and a distributed version of the algorithm is developed that enables a number of machines to work efficiently on the same problem.
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Published date: 1997
Additional Information:
Address: Southampton, UK
Organisations:
University of Southampton, Web & Internet Science
Identifiers
Local EPrints ID: 250749
URI: http://eprints.soton.ac.uk/id/eprint/250749
PURE UUID: 037d51d7-f796-4c0c-b174-be79ed815769
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Date deposited: 16 Sep 1999
Last modified: 14 Mar 2024 04:56
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Contributors
Author:
Danius T. Michaelides
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