Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1153029 | Statistical Methodology | 2016 | 15 Pages |
Abstract
Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Chantal Larose, Ofer Harel, Katarzyna Kordas, Dipak K. Dey,