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Model-based approach for household clustering with mixed scale variables

Author

Listed:
  • Christian Carmona

    (University of Oxford)

  • Luis Nieto-Barajas

    (ITAM)

  • Antonio Canale

    (University of Padua)

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.

Suggested Citation

  • Christian Carmona & Luis Nieto-Barajas & Antonio Canale, 2019. "Model-based approach for household clustering with mixed scale variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 559-583, June.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:2:d:10.1007_s11634-018-0313-6
    DOI: 10.1007/s11634-018-0313-6
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    References listed on IDEAS

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    Cited by:

    1. Corradin, Riccardo & Nieto-Barajas, Luis Enrique & Nipoti, Bernardo, 2022. "Optimal stratification of survival data via Bayesian nonparametric mixtures," Econometrics and Statistics, Elsevier, vol. 22(C), pages 17-38.

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