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A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models
Joint Authors
Source
ISRN Computational Mathematics
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-04-26
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Beh, Eric J.& Farver, Thomas B.. 2012. A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models. ISRN Computational Mathematics،Vol. 2012, no. 2012, pp.1-8.
https://search.emarefa.net/detail/BIM-468878
Modern Language Association (MLA)
Beh, Eric J.& Farver, Thomas B.. A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models. ISRN Computational Mathematics No. 2012 (2012), pp.1-8.
https://search.emarefa.net/detail/BIM-468878
American Medical Association (AMA)
Beh, Eric J.& Farver, Thomas B.. A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models. ISRN Computational Mathematics. 2012. Vol. 2012, no. 2012, pp.1-8.
https://search.emarefa.net/detail/BIM-468878
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-468878