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

Eric J. Beh, Thomas B Farver

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)475-484
Number of pages10
JournalAustralian and New Zealand Journal of Statistics
Volume54
Issue number4
DOIs
StatePublished - 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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