Advertisement
Published in:
2005 | OriginalPaper | Chapter
Bootstrapping Latent Class Models
Author : José G. Dias
Published in: Classification — the Ubiquitous Challenge
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This paper deals with improved measures of statistical accuracy for parameter estimates of latent class models. It introduces more precise confidence intervals for the parameters of this model, based on parametric and nonparametric bootstrap. Moreover, the label-switching problem is discussed and a solution to handle it introduced. The results are illustrated using a well-known dataset.