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Noncrossing structured additive multiple-output Bayesian quantile regression models

Santos, Bruno, Kneib, Thomas (2020) Noncrossing structured additive multiple-output Bayesian quantile regression models. Statistics and Computing, 30 (4). pp. 855-869. ISSN 0960-3174. E-ISSN 1573-1375. (doi:10.1007/s11222-020-09925-x) (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:90520)

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/10.1007/s11222-020-09925-x

Abstract

Quantile regression models are a powerful tool for studying different points of the conditional distribution of univariate response variables. Their multivariate counterpart extension though is not straightforward, starting with the definition of multivariate quantiles. We propose here a flexible Bayesian quantile regression model when the response variable is multivariate, where we are able to define a structured additive framework for all predictor variables. We build on previous ideas considering a directional approach to define the quantiles of a response variable with multiple outputs, and we define noncrossing quantiles in every directional quantile model. We define a Markov chain Monte Carlo (MCMC) procedure for model estimation, where the noncrossing property is obtained considering a Gaussian process design to model the correlation between several quantile regression models. We illustrate the results of these models using two datasets: one on dimensions of inequality in the population, such as income and health; the second on scores of students in the Brazilian High School National Exam, considering three dimensions for the response variable.

Item Type: Article
DOI/Identification number: 10.1007/s11222-020-09925-x
Uncontrolled keywords: Bayesian quantile regression models; Response variable with multiple outputs; Noncrossing conditional quantiles; Structured additive predictors; Income and health inequality; Brazilian High School National Exam
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: 01 Oct 2021 13:24 UTC
Last Modified: 04 Mar 2024 15:44 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90520 (The current URI for this page, for reference purposes)

University of Kent Author Information

Santos, Bruno.

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