Nonlinear Effects in the Generalized Latent Variable Model
Until recently, latent variable models such as the factor analysis model for metric responses, the two-parameter logistic model for binary responses, the multinomial model for nominal responses considered only main effects of latent variables without allowing for interaction or polynomial latent variable effects. However, nonlinear relationships among the latent variables might be necessary in real applications. Methods for fitting models with nonlinear latent terms have been developed mainly under the structural equation modelling approach. In this paper, we consider a general latent variable model framework for mixed responses (metric and categorical) that allows inclusion of both nonlinear latent and covariate effects. The model parameters are estimated using full Maximum Likelihood based on a hybrid integration-maximization algorithm. Finally, a new method for obtaining factor scores based on Multiple Imputation is proposed here for the model with nonlinear terms.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||04 Jul 2006|
|Date of revision:|
|Contact details of provider:|| Web page: http://comp-econ.org/|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:sce:scecfa:518. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.