Latent Variable Models in Heterogeneous Spaces for Observations of Mixed Types |
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Authors: | Ernest Fokoué |
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Institution: | 1. Department of Mathematics , Kettering University , Flint, Michigan, USA efokoue@kettering.edu |
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Abstract: | The maximum likelihood approach to the estimation of factor analytic model parameters most commonly deals with outcomes that are assumed to be multivariate Gaussian random variables in a homogeneous input space. In many practical settings, however, many studies needing factor analytic modeling involve data that, not only are not multivariate Gaussian variables, but also come from a partitioned input space. This article introduces an extension of the maximum likelihood factor analysis that handles multivariate outcomes made up of attributes with different probability distributions, and originating from a partitioned input space. An EM Algorithm combined with Fisher Scoring is used to estimate the parameters of the derived model. |
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Keywords: | Conditional independence EM algorithm Factor analysis Finite mixtures Fisher score Generalized linear model Latent variable models Monte Carlo Non Gaussian observations |
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