Variable Assessment in Latent Class Models.
Comput Stat Data Anal
; 77: 146-156, 2014 Sep 01.
Article
em En
| MEDLINE
| ID: mdl-24910486
ABSTRACT
The latent class model provides an important platform for jointly modeling mixed-mode data - i.e., discrete and continuous data with various parametric distributions. Multiple mixed-mode variables are used to cluster subjects into latent classes. While the mixed-mode latent class analysis is a powerful tool for statisticians, few studies are focused on assessing the contribution of mixed-mode variables in discriminating latent classes. Novel measures are derived for assessing both absolute and relative impacts of mixed-mode variables in latent class analysis. Specifically, the expected posterior gradient and the Kolmogorov variation of the posterior distribution, as well as related properties are studied. Numerical results are presented to illustrate the measures.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article