THE BOX-COX TRANSFORMATION AND NON-ITERATIVE ESTIMATION METHODS FOR ORDINAL LOG-LINEAR MODELS

被引：4
|
作者：
Beh, Eric J. [1 ]
Farver, Thomas B. [2 ]
机构：
[1] Univ Newcastle, Sch Math & Phys Sci, Callaghan, NSW 2308, Australia
[2] Univ Calif Davis, Dept Populat Hlth & Reprod, Sch Vet Med, Davis, CA 95616 USA
来源：
AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS | 2012年 / 54卷 / 04期
关键词：
Box-Cox transformation; linear-by-linear association; Newton's unidimensional method; ordinal log-linear model; ASSOCIATION; CLASSIFICATIONS;
D O I：
10.1111/anzs.12007
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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 BoxCox transformation. We also show that by using this transformation, accurate non-iterative estimates are achievable even when a contingency table contains sampling zeros.
引用
收藏
页码：475 / 484
页数：10
相关论文
共 30 条
123