多元二元分布的方向可折叠参数化

Q Mathematics Statistical Methodology Pub Date : 2015-11-01 DOI:10.1016/j.stamet.2015.07.001
Tamás Rudas
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引用次数: 4

摘要

比值比和对数线性参数是不可折叠的,这意味着在分析中包括一个变量或从中省略一个变量,可能会改变剩余变量之间的关联强度。甚至连联想的方向也可能被逆转,这一事实经常被称为辛普森悖论。如果这种反转不能发生,关联参数在方向上是可折叠的。研究了方向可折叠的关联参数的存在性。结果表明,在两个简单的假设条件下,没有任何关联参数可以定向折叠,而关联参数只取决于条件分布,就像比值比一样。主要结果是,每个方向可折叠的关联参数都给出了与单元格概率线性对比相同的关联方向。处理辛普森悖论的含义是,只有一种方法可以将任何表中的方向与关联关联起来,这样悖论就永远不会发生。
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Directionally collapsible parameterizations of multivariate binary distributions

Odds ratios and log-linear parameters are not collapsible, which means that including a variable into the analysis or omitting one from it, may change the strength of association among the remaining variables. Even the direction of association may be reversed, a fact that is often discussed under the name of Simpson’s paradox. A parameter of association is directionally collapsible, if this reversal cannot occur. The paper investigates the existence of parameters of association which are directionally collapsible. It is shown, that subject to two simple assumptions, no parameter of association, which depends only on the conditional distributions, like the odds ratio does, can be directionally collapsible. The main result is that every directionally collapsible parameter of association gives the same direction of association as a linear contrast of the cell probabilities does. The implication for dealing with Simpson’s paradox is that there is exactly one way to associate direction with the association in any table, so that the paradox never occurs.

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来源期刊
Statistical Methodology
Statistical Methodology STATISTICS & PROBABILITY-
CiteScore
0.59
自引率
0.00%
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0
期刊介绍: Statistical Methodology aims to publish articles of high quality reflecting the varied facets of contemporary statistical theory as well as of significant applications. In addition to helping to stimulate research, the journal intends to bring about interactions among statisticians and scientists in other disciplines broadly interested in statistical methodology. The journal focuses on traditional areas such as statistical inference, multivariate analysis, design of experiments, sampling theory, regression analysis, re-sampling methods, time series, nonparametric statistics, etc., and also gives special emphasis to established as well as emerging applied areas.
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