A1449
Title: Choice of number of factors and clusters in Bayesian clustering factor models
Authors: Allison Tegge - Virginia Tech (United States) [presenting]
Marco Ferreira - Virginia Tech (United States)
Hwasoo Shin - Virginia Tech (United States)
Abstract: A framework for concomitant dimension reduction and clustering based on a novel class of Bayesian clustering factor models (BCFMs) was previously introduced. BCFMs assume a factor model structure where the vectors of common factors follow a mixture of Gaussian distributions. The aim is to propose an information criterion to select the number of clusters and the number of factors. When compared to previously published competitor methods, this information criterion has favorable performance in terms of the correct selection of the number of clusters and the number of factors. Finally, the application of the information criterion is illustrated with a BCFM analysis of health care data.
