Income modeling with the Weibull mixtures
Document Type
Article
Publication Date
6-1-2022
Abstract
In this paper, we introduce six Weibull based mixture distributions to model income data. Several statistical properties of these models are derived and their closed forms are presented. The mixture model parameters are estimated using the maximum likelihood method and their performances are assessed with respect to average income per tax unit data for ten countries using information based criteria approaches as well as graphical observations. In addition, we provide application of these models to two popular inequality measures, the Gini and Bonferroni indexes as well as the common generalized entropy index. Analytic expressions of the poverty measures are given for head-count ratio and poverty-gap ratio. All the mixture models show good fit to the data with close proximity to empirical Gini and Bonferroni indexes in almost all ten countries where the income data sets are studied.
Keywords
Weibull mixture models, maximum likelihood estimation, income data, information criteria
Divisions
MathematicalSciences
Funders
Universiti Malaya (Grant No. GPF028B-2018)
Publication Title
Communications In Statistics-Theory And Methods
Volume
51
Issue
11
Publisher
Taylor & Francis Inc
Publisher Location
530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA