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Bayesian nonparametric quantile regression using splines / Paul Thompson, Yuzhi Cai, Rana Moyeed, Dominic Reeve, Julian Stander

Computational Statistics and Data Analysis, Volume: 54, Issue: 4, Pages: 1138 - 1150

Swansea University Authors: Yuzhi Cai, Dominic Reeve

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Abstract

A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means...

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Published in: Computational Statistics and Data Analysis
ISSN: 0167-9473
Published: Elsevier 2010
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URI: https://cronfa.swan.ac.uk/Record/cronfa7005
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Abstract: A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means of a Markov chain Monte Carlo algorithm. Examples of the application of the new technique to two real environmental data sets and to simulated data for which polynomial modelling is inappropriate are given. An aid for making a good choice of proposal density in the MetropolisHastings algorithm is discussed. The new nonparametric methodology provides more flexible modelling than the currently used Bayesian parametric quantile regression approach.
Keywords: non-parametric method, quantile regression, natural cubic splines, Bayesian method
College: School of Management
Issue: 4
Start Page: 1138
End Page: 1150