Some Tests for Categorical Data

Adaptive Tests for Ordered Categorical Data

Graph-based Tests for Two-sample Comparisons of Categorical Data

We study the problem of two-sample comparison with categorical data when the contingency table is sparsely populated. In modern applications, the number of categories is often comparable to the sample size, causing existing methods to have low power. When the number of categories is large, there is often underlying structure on the sample space that can be exploited. We propose a general non-pa...

Working Paper Series Categorical Data Categorical Data

Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and...

Ignorability for categorical data

We study the problem of ignorability in likelihood-based inference from incomplete categorical data. Two versions of the coarsened at random assumption (car) are distinguished, their compatibility with the parameter distinctness assumption is investigated, and several conditions for ignorability that do not require an extra parameter distinctness assumption are established. It is shown that car...

Some Remarks on Latent Variable Models in Categorical Data Analysis

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