Bayesian factor models for multivariate categorical data obtained from questionnaires

Simplex Factor Models for Multivariate Unordered Categorical Data.

Gaussian latent factor models are routinely used for modeling of dependence in continuous, binary, and ordered categorical data. For unordered categorical variables, Gaussian latent factor models lead to challenging computation and complex modeling structures. As an alternative, we propose a novel class of simplex factor models. In the single-factor case, the model treats the different categori...

Bayesian Models for Categorical Data

Bayesian Simultaneous Edit and Imputation for Multivariate Categorical Data

In categorical data, it is typically the case that some combinations of variables are theoretically impossible, such as a three year old child who is married or a man who is pregnant. In practice, however, reported values often include such structural zeros due to, for example, respondent mistakes or data processing errors. To purge data of such errors, many statistical organizations use a proc...

Bayesian Graphical Models for Multivariate Functional Data

Graphical models express conditional independence relationships among variables. Although methods for vector-valued data are well established, functional data graphical models remain underdeveloped. By functional data, we refer to data that are realizations of random functions varying over a continuum (e.g., images, signals). We introduce a notion of conditional independence between random func...

Multilevel Bayesian Models of Categorical Data Annotation

Abstract This paper demonstrates the utility of multilevel Bayesian models of data annotation for classifiers (also known as coding or rating). The observable data is the set of categorizations of items by annotators (also known as raters or coders) from which data may be missing at random or may be replicated (that is, it handles fixed panel and varying panel designs). Estimated model paramete...