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Social Indicators Research

Erschienen in:

10.06.2020 | Original Research

Bayesian Networks Model Averaging for Bes Indicators

verfasst von: Pierpaolo D’Urso, Vincenzina Vitale

Erschienen in: Social Indicators Research | Ausgabe 3/2020

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Abstract

The measure of the equitable and sustainable well-being (Bes) is of growing interest in the last years. The National Institute of Statistics (Istat) provides, for Italy, a wide set of indicators describing each domain of well-being that is, by definition, a multidimensional concept. In this study, we propose the use of Bayesian networks to deal with basic and composite Bes indicators. Its capability to model very complex multivariate dependence structures is useful to describe the relationships between indicators belonging to different domains and, being a probabilistic expert system, the estimated network could be also useful for probabilistic inference and what-if analysis. In this study, all the Bayesian networks structures have been estimated by means of the hill climbing algorithm based on bootstrap resampling and model averaging in order to prevent bias due to deviations from the normality assumption.

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They are not probabilistic nodes; they allow to defines mathematical expressions such, in this case, the arithmetic mean of its parents nodes.

The composite indicator EMPLOYMENT is a probabilistic node in the network since it is composed of only one basic indicator.

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Titel: Bayesian Networks Model Averaging for Bes Indicators
verfasst von: Pierpaolo D’Urso
Vincenzina Vitale
Publikationsdatum: 10.06.2020
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 3/2020
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-020-02401-z

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