基于方形或然率表中累积边际概率的边际均值偏离度量

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY
Metrika Pub Date : 2024-02-05 DOI:10.1007/s00184-023-00945-x
Shuji Ando
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引用次数: 0

摘要

本研究提出了一种测量方法,可以同时评估有序类别方差表中边际均值相等(ME)模型的偏离程度和方向。所提出的测量方法是行和列累积边际概率的函数。当 ME 模型不适合数据时,我们就会对测量 ME 模型的偏离程度感兴趣,因为比 ME 模型限制更弱的模型只是饱和模型。现有的这一测量方法表示 ME 模型的偏离程度,它不依赖于观测值落在表格主对角线单元格中的概率。对于观测值集中在主对角线单元格中的数据,现有的度量方法可能会高估 ME 模型的偏离程度。建议的测量方法可以解决这个问题。本研究使用 delta 方法为所提出的测量方法推导出一个估计值和一个近似置信区间。提出的测量方法可用于比较两个数据集中 ME 模型的偏离程度。通过应用于临床试验的真实数据,对所提出的测量方法的实用性进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measure of deviancy from marginal mean equality based on cumulative marginal probabilities in square contingency tables

This study proposes a measure that can concurrently evaluate the degree and direction of deviancy from the marginal mean equality (ME) model in square contingency tables with ordered categories. The proposed measure is constructed as the function of the row and column cumulative marginal probabilities. When the ME model does not fit data, we are interested in measuring the degree of deviancy from the ME model, because the model having weaker restrictions than the ME model is only the saturated model. This existing measure, which represents the degree of deviancy from the ME model, does not depend on the probabilities that observations will fall in the main diagonal cells of the table. For the data in which observations are concentrated in the main diagonal cells, the existing measure may overestimate the degree of deviancy from the ME model. The proposed measure can address this issue. This study derives an estimator and an approximate confidence interval for the proposed measure using the delta method. The proposed measure would be utility for comparing degrees of deviancy from the ME model in two datasets. The proposed measure is evaluated the usefulness with the application to real data of clinical trials.

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来源期刊
Metrika
Metrika 数学-统计学与概率论
CiteScore
1.50
自引率
14.30%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.
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