On estimating the linear-by-linear parameter for ordinal log-linear models: a computational study

Title: On estimating the linear-by-linear parameter for ordinal log-linear models: a computational study
Creator: Beh, Eric J.; Farver, Thomas B.
Relation: ISRN Computational Mathematics
Publisher Link: http://dx.doi.org/10.5402/2012/340415
Publisher: Hindawi Publishing Corporation
Resource Type: journal article
Date: 2012
Description: Estimating linear-by-linear association has long been an important topic in the analysis of contingency tables. For ordinal variables, log-linear models may be used to detect the strength and magnitude of the association between such variables, and iterative procedures are traditionally used. Recently, studies have shown, by way of example, three non-iterative techniques can be used to quickly and accurately estimate the parameter. This paper provides a computational study of these procedures, and the results show that they are extremely accurate when compared with estimates obtained using Newton’s unidimensional method.
Subject: log-linear models; mathematical models; Newton; contingency tables
Identifier: http://hdl.handle.net/1959.13/1051985
Identifier: uon:15347
Identifier: ISSN:2090-7842
Language: eng
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