On Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models: A Computational Study. (28th May 2012)

• Record Type: Journal Article
• Authors: Beh, Eric J.; Farver, Thomas B.
• Other Names: Allahviranloo T. Academic Editor.; Ruskin H. J. Academic Editor.; Vasconcelos P. B. Academic Editor.; Wang Q.-W. Academic Editor.
• Is Part Of: ISRN computational mathematics. Volume 2012(2012)
• Publication Date: 2012-05-28
• Subjects: Numerical analysis -- Periodicals; Mathematics -- Data processing -- Periodicals; Mathematics -- Data processing; Numerical analysis; Electronic journals; Periodicals; 510
• DOI: 10.5402/2012/340415
• Languages: English
• ISSNs: 2090-7842

Abstract

Abstract : 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.