Summary
Non-linear latent variable models are specified that include squares and interactions of latent regressor variables, observed regressors and missing data as special cases. To estimate the parameters, the models are put in a Bayesian framework. The posterior distributions of the parameters and the latent variables are estimated using Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hastings algorithm. The proposed estimation methods are illustrated by two simulated examples.
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© 1997 Springer-Verlag Berlin Heidelberg
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Wittenberg, J., Arminger, G. (1997). Bayesian Non-Linear Latent Variable Models. In: Klar, R., Opitz, O. (eds) Classification and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59051-1_25
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DOI: https://doi.org/10.1007/978-3-642-59051-1_25
Publisher Name: Springer, Berlin, Heidelberg
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