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- Title
An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models.
- Authors
Vermunt, Jeroen K.
- Abstract
It is shown how to implement an EM algorithm for maximum likelihood estimation of hierarchical nonlinear models for data sets consisting of more than two levels of nesting. This upward–downward algorithm makes use of the conditional independence assumptions implied by the hierarchical model. It cannot only be used for the estimation of models with a parametric specification of the random effects, but also to extend the two-level nonparametric approach – sometimes referred to as latent class regression – to three or more levels. The proposed approach is illustrated with an empirical application.
- Subjects
ALGORITHMS; NONLINEAR statistical models; MATHEMATICAL models; MULTILEVEL models; EMPIRICAL research; REGRESSION analysis
- Publication
Statistica Neerlandica, 2004, Vol 58, Issue 2, p220
- ISSN
0039-0402
- Publication type
Article
- DOI
10.1046/j.0039-0402.2003.00257.x