Latent variable (LV) models (see, e.g. Bartholomew et al., 2011) have found an important field of application in the context of life sciences. Their use is justified by the complexity of the biological systems, with implications in terms of sophisticated dependencies between observable variables. In LV models, as it is well known, the observable response variables are affected by (discrete or continuous) variables that are not directly observed. LV models are based on specific assumptions on the conditional distribution of the response variables, given the latent ones. This allows us to model the effect of unobservable covariates (factors) and, thus, to account for the unobserved heterogeneity between subjects.

Editorial: Special section on latent variable models for longitudinal data / Bartolucci, Francesco; Giordani, Paolo. - In: BIOMETRICAL JOURNAL. - ISSN 0323-3847. - 59:4(2017), pp. 781-782. [10.1002/bimj.201700041]

Editorial: Special section on latent variable models for longitudinal data

GIORDANI, Paolo
2017

Abstract

Latent variable (LV) models (see, e.g. Bartholomew et al., 2011) have found an important field of application in the context of life sciences. Their use is justified by the complexity of the biological systems, with implications in terms of sophisticated dependencies between observable variables. In LV models, as it is well known, the observable response variables are affected by (discrete or continuous) variables that are not directly observed. LV models are based on specific assumptions on the conditional distribution of the response variables, given the latent ones. This allows us to model the effect of unobservable covariates (factors) and, thus, to account for the unobserved heterogeneity between subjects.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/997865
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