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Bayesian analysis for zero-or-one inflated proportion data using quantile regression

Santos, Bruno R., Bolfarine, Heleno (2015) Bayesian analysis for zero-or-one inflated proportion data using quantile regression. Journal of Statistical Computation and Simulation, 85 (17). pp. 3579-3593. ISSN 0094-9655. E-ISSN 1563-5163. (doi:10.1080/00949655.2014.986733) (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:90513)

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:
https://doi.org/10.1080/00949655.2014.986733

Abstract

In this paper, we propose the use of Bayesian quantile regression for the analysis of proportion data. We also consider the case when the data present a zero-or-one inflation using a two-part model approach. For the latter scheme, we assume that the response variable is generated by a mixed discrete–continuous distribution with a point mass at zero or one. Quantile regression is then used to explain the conditional distribution of the continuous part between zero and one, while the mixture probability is also modelled as a function of the covariates. We check the performance of these models with two simulation studies. We illustrate the method with data about the proportion of households with access to electricity in Brazil.

Item Type: Article
DOI/Identification number: 10.1080/00949655.2014.986733
Uncontrolled keywords: Bayesian quantile regression; proportion data; two-part model; proportion of households with access to electricity in Brazil
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 09:06 UTC
Last Modified: 17 Aug 2022 11:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90513 (The current URI for this page, for reference purposes)

University of Kent Author Information

Santos, Bruno R..

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