The Box-Cox Transformation and Non-Iterative Estimation Methods For Ordinal Log-Linear Models

Eric J. Beh; Thomas B Farver

doi:10.1111/anzs.12007

The Box-Cox Transformation and Non-Iterative Estimation Methods For Ordinal Log-Linear Models

Eric J. Beh, Thomas B Farver

Population Health and Reproduction

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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	https://doi.org/10.1111/anzs.12007
State	Published - Dec 2012

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

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The Box-Cox Transformation and Non-Iterative Estimation Methods For Ordinal Log-Linear Models

Abstract

Keywords

ASJC Scopus subject areas

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