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.

在部分实际应用场景中，仅可观测到分类结局变量的粗化版本。基于极大似然法的参数推断在原理上具备可行性，但标准软件工具无法支持其计算实现。本文提出两款统计命令，可助力该场景下针对分类结局的各类参数模型（涵盖名义尺度与有序尺度两种情形）开展极大似然估计。特别地，本文还覆盖了结局变量可能取值附带概率信息的情形。文中还给出并分析了两个可阐释该应用场景的示例。