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- W951838442 abstract "In this chapter, we introduce a class of longitudinal latent processes where, at any time, the latent variable is assumed to be indirectly observable, through a set of categorical binary variables (items). We assume that the measurement model relating the observed items and the latent variable, at each time, is a Rasch model. Besides the description of these models, the objective aimed in this work, is the estimation of the parameters of the model by maximum likelihood method via an EM algorithm. We consider more deeply two classes of distribution for the longitudinal latent process: (1) the General Latent Markov process and a special case, (2) the latent autoregressive process of order one." @default.
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- W951838442 date "2010-01-01" @default.
- W951838442 modified "2023-09-25" @default.
- W951838442 title "Longitudinal Latent Markov Processes Observable Through an Invariant Rasch Model" @default.
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- W951838442 doi "https://doi.org/10.1007/978-0-8176-4971-5_6" @default.
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