基于嵌套几何的列联表模型估计方法

Yoshihiro Hirose, F. Komaki
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引用次数: 2

摘要

提出了一种基于几何思想的列联表模型参数估计方法。我们的方法-列联表的平分线回归(BRCT) -基于列联表模型的嵌套结构。我们的方法在估计或消除高阶相互作用后估计低阶相互作用对应的参数。brct生成一系列参数估计,其中的每个元素表示一个模型和一个参数估计。序列的长度等于参数的个数,这比模型的总数要小得多。我们描述了brc算法并给出了一个示例。我们提供了两种情况的解释:(a)两个因素和(b) K个因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Estimation Procedure for Contingency Table Models Based on Nested Geometry
We propose a method for estimating the parameters of contingency table models, which is motivated by a geometrical idea. Our method—bisector regression for contingency tables (BRCT)—is based on a nested structure of contingency table models. Our method estimates parameters corresponding to the interactions of lower orders after estimating or eliminating those of higher orders. BRCTgenerates a sequence of parameter estimates, each element of which represents a model and a parameter estimate. The length of the sequence is equal to the number of parameters, which is much smaller than the total number of models. We describe the BRCTalgorithm and show an example. We provide explanations for two cases: (a) two factors and (b) K factors.
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