To analyse telecommunications marketing data which are usually made of discrete and continuous observations we consider a general framework to jointly model continuous, count and categorical variables under a nonparametric prior, which is induced through rounding latent variables having an unknown density with respect to Lebesgue measure. The approach is applied to model the joint density of traffic data for a portion of customers of an European mobile phone operator.

A Bayesian nonparametric model for data on different scales of measure; an application to customer base management of telecommunications companies.

CANALE, Antonio;
2013-01-01

Abstract

To analyse telecommunications marketing data which are usually made of discrete and continuous observations we consider a general framework to jointly model continuous, count and categorical variables under a nonparametric prior, which is induced through rounding latent variables having an unknown density with respect to Lebesgue measure. The approach is applied to model the joint density of traffic data for a portion of customers of an European mobile phone operator.
2013
Advances in Latent Variables - Methods, Models and Applications
Brescia
19-21/6/2013
Advances in Latent Variables
Vita e Pensiero - Pubblicazioni dell'Università Cattolica del Sacro Cuore
1
5
978 88 343 2556 8
http://meetings.sis-statistica.org/index.php/sis2013/ALV/paper/view/2671
Mixed discrete and continuous; Nonparametric regression; Zero-inflated models
Antonio Canale; David B. Dunson
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/139498
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