Latent Class Models
| Autor: | Vermunt, J.K., Peterson, P., Baker, E., McGaw, B. |
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| Přispěvatelé: | Department of Methodology and Statistics |
| Rok vydání: | 2010 |
| Předmět: |
Mixture model
Probabilistic latent semantic analysis Statistical software Model selection Statistical model Latent variable computer.software_genre Latent class model Scaling models Cluster analysis Mixture regression Latent class analysis Finite-mixture model Econometrics Mixture growth models Latent profile models Random-effects modeling Data mining Latent variable model computer Latent markov models Mathematics |
| Zdroj: | International encyclopedia of education, 238-244 ISSUE=Third;STARTPAGE=238;ENDPAGE=244;TITLE=International encyclopedia of education STARTPAGE=238;ENDPAGE=244;TITLE=International encyclopedia of education |
| DOI: | 10.1016/b978-0-08-044894-7.01340-3 |
| Popis: | A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, LCs, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. This article describes simple LC models for clustering, restricted LC models for scaling, and mixture regression models for nonparametric random-effects modeling, as well as gives an overview of recent developments in the field of LC analysis. Moreover, attention is paid to topics such as maximum likelihood estimation, identification issues, model selection, and software. |
| Databáze: | OpenAIRE |
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