Skip to main content
Kent Academic Repository

Analysis of Brazil’s presidential election via Bayesian spatial quantile regression

Santos, Bruno R., Bolfarine, Heleno (2015) Analysis of Brazil’s presidential election via Bayesian spatial quantile regression. In: Proceedings of the 30th International Workshop on Statistical Modelling. 2. pp. 247-248. Johannes Kepler University Linz (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90515)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://www.statmod.org/files/proceedings/iwsm2015_...

Abstract

We show an extension of Bayesian quantile regression models when the response variable is reported as a proportion and spatial correlation is present. We are specially interested in the data of the last presidential election in Brazil, which was decided by 2% of the valid votes, approximately. We use quantile regression models to show how some sociodemographic variables are associated with different quantiles of the distribution of votes in this close election.

Item Type: Conference or workshop item (Poster)
Uncontrolled keywords: Bayesian spatial quantile regression; Asymmetric Laplace predictive process; Brazil’s presidential election data.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA297 Numerical analysis
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Amy Boaler
Date Deposited: 01 Oct 2021 10:05 UTC
Last Modified: 17 Aug 2022 11:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90515 (The current URI for this page, for reference purposes)

University of Kent Author Information

Santos, Bruno R..

Creator's ORCID:
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.