Volume 3, 2020
|Number of page(s)||9|
|Section||Mathematics - Applied Mathematics|
|Published online||11 September 2020|
A copula-based quantifying of the relationship between race inequality among neighbourhoods in São Paulo and age at death
Department of Statistics, University of Campinas, Sergio Buarque de Holanda, 651, 13083-859 Campinas, S.P., Brazil
* Corresponding author: firstname.lastname@example.org
Accepted: 13 August 2020
In this paper, we combine two statistical tools with the objective of creating models that represent the dependence between (i) the proportion of the black/brown population in relation to the total population of a neighborhood (pct) and (ii) the average age at which people died in the neighborhood (age). We explore the dependence between pct and age in São Paulo city, Brazil, during 2018. The statistical tools are models of copulas and informative and non-informative settings according to the Bayesian perspective. The different scenarios and models allow us to delineate the dependence between pct and age, and, through the Bayesian Information Criterion we can indicate which of these models best represents the data. The approach implemented here allows us to define estimates of variations in life expectancy conditioned by percentage intervals of pct. With them, we can conclude that on average all the scenarios point to a decrease in life expectancy by increasing the proportion of pct. When conditioning the percentages of pct to 4 intervals (0, 0.25], (0.25, 0.5], (0.5, 0.75], (0.75, 1] respectively, we note that the expectation is reduced in average at a constant rate from one interval in comparison with the immediate and next interval from left to right in [0, 1].
Key words: Copula models / Frank copula / Bayesian estimation / Conditional expectancy
© V.A. González-López and R. Rodrigues de Moraes, Published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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