A factor mixture analysis model for multivariate binary data

A parametric mixture model for clustering multivariate binary data

The traditional latent class analysis (LCA) uses a mixture model with binary responses on each subject that are independent conditional on cluster membership. However, in many practical applications, the responses are correlated because they are observed on the same subject; this is known as local dependence. In this paper, we extend the LCA model to allow for local dependence in each cluster t...

Screening for a Multivariate Mixture Normal Distribution

Multivariate Data Clustering for the Gaussian Mixture Model

This paper discusses a soft sample clustering problem for multivariate independent random data satisfying the mixture model of the Gaussian distribution. The theory recommends to estimate the parameters of model by the maximum likelihood method and to use “plug-in” approach for data clustering. Unfortunately, the calculation problem of the maximum likelihood estimate is not completely solved in...

Bayesian model determination for multivariate ordinal and binary data

We consider how to compare different conditional independence specifications for ordinal categorical data, by calculating a posterior distribution over classes of graphical models. The approach is based on the multivariate ordinal probit model (Chib and Greenberg, 1998) where the data are considered to have arisen as truncated multivariate normal random vectors. By parameterising the precision ...

Describing Disability through Individual-level Mixture Models for Multivariate Binary Data.

Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social Security for a growing population of elderly adults. We consider an extract of functional disability data from the National Long Term Care Survey (NLTCS) and attempt to develop disability profiles using variations of the Grade of Membership (GoM) mode...