Abstract
Summary Parameter estimation for association and log-linear models is an important aspect of the analysis of cross-classified categorical data. Classically, iterative procedures, including Newton's method and iterative scaling, have typically been used to calculate the maximum likelihood estimates of these parameters. An important special case occurs when the categorical variables are ordinal and this has received a considerable amount of attention for more than 20 years. This is because models for such cases involve the estimation of a parameter that quantifies the linear-by-linear association and is directly linked with the natural logarithm of the common odds ratio. The past five years has seen the development of non-iterative procedures for estimating the linear-by-linear parameter for ordinal log-linear models. Such procedures have been shown to lead to numerically equivalent estimates when compared with iterative, maximum likelihood estimates. Such procedures also enable the researcher to avoid some of the computational difficulties that commonly arise with iterative algorithms. This paper investigates and evaluates the performance of three non-iterative procedures for estimating this parameter by considering 14 contingency tables that have appeared in the statistical and allied literature. The estimation of the standard error of the association parameter is also considered.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 335-352 |
| Number of pages | 18 |
| Journal | Australian and New Zealand Journal of Statistics |
| Volume | 51 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 2009 |
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Keywords
- Association model
- Contingency table
- Newton's method
- Ordered categorical variable
- Orthogonal polynomials
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
AN evaluation of non-iterative methods for estimating the linear-by-linear parameter of ordinal log-linear models. / Beh, Eric J.; Farver, Thomas B.
In: Australian and New Zealand Journal of Statistics, Vol. 51, No. 3, 09.2009, p. 335-352.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - AN evaluation of non-iterative methods for estimating the linear-by-linear parameter of ordinal log-linear models
AU - Beh, Eric J.
AU - Farver, Thomas B
PY - 2009/9
Y1 - 2009/9
N2 - Summary Parameter estimation for association and log-linear models is an important aspect of the analysis of cross-classified categorical data. Classically, iterative procedures, including Newton's method and iterative scaling, have typically been used to calculate the maximum likelihood estimates of these parameters. An important special case occurs when the categorical variables are ordinal and this has received a considerable amount of attention for more than 20 years. This is because models for such cases involve the estimation of a parameter that quantifies the linear-by-linear association and is directly linked with the natural logarithm of the common odds ratio. The past five years has seen the development of non-iterative procedures for estimating the linear-by-linear parameter for ordinal log-linear models. Such procedures have been shown to lead to numerically equivalent estimates when compared with iterative, maximum likelihood estimates. Such procedures also enable the researcher to avoid some of the computational difficulties that commonly arise with iterative algorithms. This paper investigates and evaluates the performance of three non-iterative procedures for estimating this parameter by considering 14 contingency tables that have appeared in the statistical and allied literature. The estimation of the standard error of the association parameter is also considered.
AB - Summary Parameter estimation for association and log-linear models is an important aspect of the analysis of cross-classified categorical data. Classically, iterative procedures, including Newton's method and iterative scaling, have typically been used to calculate the maximum likelihood estimates of these parameters. An important special case occurs when the categorical variables are ordinal and this has received a considerable amount of attention for more than 20 years. This is because models for such cases involve the estimation of a parameter that quantifies the linear-by-linear association and is directly linked with the natural logarithm of the common odds ratio. The past five years has seen the development of non-iterative procedures for estimating the linear-by-linear parameter for ordinal log-linear models. Such procedures have been shown to lead to numerically equivalent estimates when compared with iterative, maximum likelihood estimates. Such procedures also enable the researcher to avoid some of the computational difficulties that commonly arise with iterative algorithms. This paper investigates and evaluates the performance of three non-iterative procedures for estimating this parameter by considering 14 contingency tables that have appeared in the statistical and allied literature. The estimation of the standard error of the association parameter is also considered.
KW - Association model
KW - Contingency table
KW - Newton's method
KW - Ordered categorical variable
KW - Orthogonal polynomials
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UR - http://www.scopus.com/inward/citedby.url?scp=69949096932&partnerID=8YFLogxK
U2 - 10.1111/j.1467-842X.2009.00549.x
DO - 10.1111/j.1467-842X.2009.00549.x
M3 - Article
AN - SCOPUS:69949096932
VL - 51
SP - 335
EP - 352
JO - Australian and New Zealand Journal of Statistics
JF - Australian and New Zealand Journal of Statistics
SN - 1369-1473
IS - 3
ER -