分析带有或不带有概率信息的粗糙分类数据

IF 3.2 2区 数学 Q1 SOCIAL SCIENCES, MATHEMATICAL METHODS
W. Vach, Cornelia Alder, Sandra Pichler
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引用次数: 2

摘要

在某些应用中,只能观察到分类结果变量的粗化版本。基于最大似然方法的参数推理原则上是可行的,但标准软件工具无法在计算上覆盖它。在这篇文章中,我们提出了两个命令,以便于在这种情况下对分类结果的广泛参数模型进行最大似然估计——在名义量表和序数量表的情况下。特别地,还涵盖了关于结果变量的可能值的概率信息的情况。给出并分析了两个激发这种场景的例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing coarsened categorical data with or without probabilistic information
In some applications, only a coarsened version of a categorical outcome variable can be observed. Parametric inference based on the maximum likelihood approach is feasible in principle, but it cannot be covered computationally by standard software tools. In this article, we present two commands facilitating maximum likelihood estimation in this situation for a wide range of parametric models for categorical outcomes—in the cases both of a nominal and an ordinal scale. In particular, the case of probabilistic information about the possible values of the outcome variable is also covered. Two examples motivating this scenario are presented and analyzed.
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来源期刊
Stata Journal
Stata Journal 数学-统计学与概率论
CiteScore
7.80
自引率
4.20%
发文量
44
审稿时长
>12 weeks
期刊介绍: The Stata Journal is a quarterly publication containing articles about statistics, data analysis, teaching methods, and effective use of Stata''s language. The Stata Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.
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