Ignorability for categorical data

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...

Ignorability for general longitudinal data

Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Our formulation of ignorability does not depend on any noti...

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...

Graphical Methods for Categorical Data

The graphical displays shown here are implemented in SAS/IML software whose combination of matrix operations, built-in Statistical methods for categorical data, such as loglinear models functions for contingency table analysis, and graphics provide a and logistic regression, represent discrete analogs of the analysis of convenient environment for graphical display for multiway variance and regr...

Bayesian Models for Categorical Data