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Bayesian quantile regression analysis for continuous data with a discrete component at zero

Santos, Bruno R., Bolfarine, Heleno (2018) Bayesian quantile regression analysis for continuous data with a discrete component at zero. Statistical Modelling, 18 (1). pp. 73-93. ISSN 1471-082X. E-ISSN 1477-0342. (doi:10.1177/1471082X17719633) (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:90516)

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. (Contact us about this Publication)
Official URL
https://doi.org/10.1177/1471082X17719633

Abstract

In this work, we propose a Bayesian quantile regression method to response variables with mixed discrete-continuous distribution with a point mass at zero, where these observations are believed to be left censored or true zeros. We combine the information provided by the quantile regression analysis to present a more complete description of the probability of being censored given that the observed value is equal to zero, while also studying the conditional quantiles of the continuous part. We build up a Markov Chain Monte Carlo method from related models in the literature to obtain samples from the posterior distribution. We demonstrate the suitability of the model to analyse this censoring probability with a simulated example and two applications with real data. The first is a well-known dataset from the econometrics literature about women labour in Britain, and the second considers the statistical analysis of expenditures with durable goods, considering information from Brazil.

Item Type: Article
DOI/Identification number: 10.1177/1471082X17719633
Uncontrolled keywords: asymmetric Laplace distribution; Bayesian quantile regression; Durable goods; left censoring; two-part model
Subjects: Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
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 10:52 UTC
Last Modified: 04 Oct 2021 12:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90516 (The current URI for this page, for reference purposes)
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