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Latent Class Analysis Variable Selection1

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable’s usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variab...

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Bibliografische gegevens
Hoofdauteurs: Dean, Nema, Raftery, Adrian E.
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: 2010
Onderwerpen:
Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC2934856/
https://ncbi.nlm.nih.gov/pubmed/20827439
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10463-009-0258-9
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