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A two-part model using quantile regression under a Bayesian perspective.

Santos, Bruno R., Bolfarine, Heleno (2013) A two-part model using quantile regression under a Bayesian perspective. In: Proceedings of the 28th International Workshop on Statistical Modelling. . pp. 363-374. Statistical Modelling Society ISBN 978-88-96251-47-8. (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:90495)

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.

Abstract

We develop an extension of the two-part model proposed by Cragg (1971) considering the asymmetric Laplace distribution for the continuous density, proposing a quantile regression analysis in the process, within a Bayesian approach. We also consider the case where there could be a zero inflation process while estimating a Bayesian tobit quantile regression, and by the imputation of the latent variable indicating whether a zero observation belongs to a point mass or the continuous distribution, we are able to obtain a generalization of our two-part model. We illustrate our method in a known data set in the field of econometrics.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Bayesian quantile regression; Two-part model; MCMC
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Amy Boaler
Date Deposited: 30 Sep 2021 11:54 UTC
Last Modified: 05 Nov 2024 12:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90495 (The current URI for this page, for reference purposes)

University of Kent Author Information

Santos, Bruno R..

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