Compositional analysis of a mixture distribution with application to categorical modelling
Résumé Many probability distributions can be represented as compound distributions. Consider some parameter vector as random. The compound distribution is the expected distribution of the variable of interest given the random parameters. Our idea is to define a partition of the domain of definition of the random parameters, so that we can represent the expected density of the variable of interest as a finite mixture of conditional densities. We then model the probabilities of the conditional densities using information on population categories, thus modifying the original overall model. Our examples use the European Union Statistics on Income and Living Conditions (EU-SILC) data. For each country, we estimate a mixture model derived from the GB2 in which the probability weights are predicted with household categories. Comparisons across countries are processed using compositional data analysis tools. Our method also offers an indirect estimation of inequality and poverty indices.
Mots-clés GB2 distribution, Mixture distribution, Maximum pseudolikelihood estimation, Sandwich variance estimator, Income distribution, Inequality and poverty indicators.
Citation Graf, M., & Nedyalkova, D. (2013, 10 December). Compositional analysis of a mixture distribution with application to categorical modelling. Paper presented at CodaWork 2013, Vorau, Austria.
Type Actes de congrès (Anglais)
Nom de la conférence CodaWork 2013 (Vorau, Austria)
Date de la conférence 10-12-2013
Pages 53-63
URL http://www.statistik.tuwien.ac.at/CoDaWork/CoDaWork2013Pr...
Liée au projet Convention Université de Neuchâtel/Office fédéral de la s...