Abstract
Recently Beh and Farver investigated and evaluated three non-iterative procedures for estimating the linear-by-linear parameter of an ordinal log-linear model. The study demonstrated that these non-iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box-Cox transformation. We also show that by using this transformation, accurate non-iterative estimates are achievable even when a contingency table contains sampling zeros.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 475-484 |
| Number of pages | 10 |
| Journal | Australian and New Zealand Journal of Statistics |
| Volume | 54 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2012 |
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Keywords
- Box-Cox transformation
- Linear-by-linear association
- Newton's unidimensional method
- Ordinal log-linear model
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
The Box-Cox Transformation and Non-Iterative Estimation Methods For Ordinal Log-Linear Models. / Beh, Eric J.; Farver, Thomas B.
In: Australian and New Zealand Journal of Statistics, Vol. 54, No. 4, 12.2012, p. 475-484.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The Box-Cox Transformation and Non-Iterative Estimation Methods For Ordinal Log-Linear Models
AU - Beh, Eric J.
AU - Farver, Thomas B
PY - 2012/12
Y1 - 2012/12
N2 - Recently Beh and Farver investigated and evaluated three non-iterative procedures for estimating the linear-by-linear parameter of an ordinal log-linear model. The study demonstrated that these non-iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box-Cox transformation. We also show that by using this transformation, accurate non-iterative estimates are achievable even when a contingency table contains sampling zeros.
AB - Recently Beh and Farver investigated and evaluated three non-iterative procedures for estimating the linear-by-linear parameter of an ordinal log-linear model. The study demonstrated that these non-iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box-Cox transformation. We also show that by using this transformation, accurate non-iterative estimates are achievable even when a contingency table contains sampling zeros.
KW - Box-Cox transformation
KW - Linear-by-linear association
KW - Newton's unidimensional method
KW - Ordinal log-linear model
UR - http://www.scopus.com/inward/record.url?scp=84872706561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872706561&partnerID=8YFLogxK
U2 - 10.1111/anzs.12007
DO - 10.1111/anzs.12007
M3 - Article
VL - 54
SP - 475
EP - 484
JO - Australian and New Zealand Journal of Statistics
JF - Australian and New Zealand Journal of Statistics
SN - 1369-1473
IS - 4
ER -