Nonlinear Effects in the Generalized Latent Variable Model
AbstractUntil 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.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
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.
Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 518.
Date of creation: 04 Jul 2006
Date of revision:
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral 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.