Analyzing coarsened categorical data with or without probabilistic information

Werner Vach (), Cornelia Alder () and Jorge Rivera ()
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Werner Vach: University of Basel
Cornelia Alder: University of Basel
Jorge Rivera: University of Basel

Stata Journal, 2022, vol. 22, issue 1, 158-194

Abstract: 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.

Keywords: pccfit; pccprob; coarsened data; multinomial distribution; multinomial regression; ordinal outcome variables; ordered regression; human osteoarchaeology; palaeodemography; diagnostic accuracy studies; imperfect reference standard (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:y:22:y:2022:i:1:p:158-194

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DOI: 10.1177/1536867X221083902

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