A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models | Zendy

Eric J. Beh | Zendy; Thomas B. Farver | Zendy

Open Access

A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models

Author(s) -

Eric J. Beh,

Thomas B. Farver

Publication year - 2012

Publication title -

isrn computational mathematics

Language(s) - English

Resource type - Journals

ISSN - 2090-7842

DOI - 10.5402/2012/396831

Subject(s) - contingency table , log linear model , mathematics , reliability (semiconductor) , linear model , estimation theory , maximum likelihood , statistics , generalized linear model , measure (data warehouse) , iterative method , ordinal data , algorithm , computer science , data mining , power (physics) , physics , quantum mechanics

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.

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