Gelfand, A. Genz, A. Geweke, J. Amman, D. Kendrick, and J. Amsterdam: Elsevier.
Latent variable models and factor analysis: a unified approach
Gneiting, T. Finkenstaedt, L.
Held, and V. Hogan, J. Huber, P.
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Irincheeva, I. Johnson, S. Kazianka, H.
Efficient estimation of generalized linear latent variable models
Li, H. Lopes, H. Ma, Y. Magnus, J. Minozzo, M. Montanari, A. Nelsen, R. Peng, R.
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Zhu, J. Genton 2 1. Teaching Mode Traditional lectures.
Language English. Insegnamenti online - IOL. Course Timetable from Sep 18, to Oct 22, By the end of the course the student acquires knowledge of multivariate statistical methods based on latent variable models for the analysis of categorical and continuous data. The student is also able to choose the best method to perform multivariate analyses of a given dataset and to interpret the obtained results.
85177 - LATENT VARIABLE MODELS
The normal linear factor model: specification, maximum likelihood estimation by the EM algorithm, goodness of fit. Latent trait model with polytomous and ordinal data: specification and parameter interpretation. The underlyng variable approach. Latent class model with binary data: specification, identifiability, maximum likelihood estimation, goodness of fit.
Bartholomew D. Chichester, UK : Wiley. The exam consists of a mandatory written exam.