A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models. (26th April 2012)

• Record Type: Journal Article
• Authors: Beh, Eric J.; Farver, Thomas B.
• Other Names: Miura K. T. Academic Editor.; Vasconcelos P. B. Academic Editor.; Wang Q.-W. Academic Editor.
• Is Part Of: ISRN computational mathematics. Volume 2012(2012)
• Publication Date: 2012-04-26
• Subjects: Numerical analysis -- Periodicals; Mathematics -- Data processing -- Periodicals; Mathematics -- Data processing; Numerical analysis; Electronic journals; Periodicals; 510
• DOI: 10.5402/2012/396831
• Languages: English
• ISSNs: 2090-7842

Abstract

Abstract : For ordinal log-linear models, the estimation of the parameter reflecting the linear-by-linear measure of association has long been a topic for the analysis of dependence for contingency tables. Typically, iterative procedures (including Newton's method) are used to determine the maximum likelihood estimate of the parameter. Recently Beh and Farver (2009, ANZJS, 51, 335–352) show by way of example three reliable and accurate noniterative techniques that can be used to estimate the parameter and extended this study by examining their reliability computationally. This paper further investigates the reliability of the non-iterative procedures when compared with Newton's method for estimating this association parameter and considers the impact of the sample size on the estimate.