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- W3142196258 abstract "We discuss directed acyclic graph (DAG) models to represent the indepen- dence structure of linear Gaussian systems with continuous variables: such models can be interpreted as a set of recursive univariate regressions. Then we consider Gaussian models in which one of the variables is not observed and we show how the incomplete log-likelihood of the observed data can be maximized using the EM. As the EM algo- rithm does not provide the matrix of the second derivatives we show how to get an explicit formula for the observed information matrix. We illustrate the utility of the models with some examples." @default.
- W3142196258 created "2021-04-13" @default.
- W3142196258 creator A5021943787 @default.
- W3142196258 date "2004-07-06" @default.
- W3142196258 modified "2023-10-18" @default.
- W3142196258 title "Issues on the Estimation of Latent Variable and Latent Class Models with Social Science Applications" @default.
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