Model-based approach for household clustering with mixed scale variables

Christian Carmona (), Luis Nieto-Barajas () and Antonio Canale ()
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Christian Carmona: University of Oxford
Luis Nieto-Barajas: ITAM
Antonio Canale: University of Padua

Advances in Data Analysis and Classification, 2019, vol. 13, issue 2, No 11, 559-583

Abstract: Abstract The Ministry of Social Development in Mexico is in charge of creating and assigning social programmes targeting specific needs in the population for the improvement of the quality of life. To better target the social programmes, the Ministry is aimed to find clusters of households with the same needs based on demographic characteristics as well as poverty conditions of the household. Available data consists of continuous, ordinal, and nominal variables, all of which come from a non-i.i.d complex design survey sample. We propose a Bayesian nonparametric mixture model that jointly models a set of latent variables, as in an underlying variable response approach, associated to the observed mixed scale data and accommodates for the different sampling probabilities. The performance of the model is assessed via simulated data. A full analysis of socio-economic conditions in households in the Mexican State of Mexico is presented.

Keywords: Bayes nonparametrics; Complex design; Latent variables; Multivariate normal; Poisson–Dirichlet process; 62D05; 62G86; 62P25; 62H30 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11634-018-0313-6

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