Journal article 1486 views
Bayesian nonparametric quantile regression using splines
,
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
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1016/j.csda.2009.09.004
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...
| Published in: | Computational Statistics and Data Analysis |
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| ISSN: | 0167-9473 |
| Published: |
Elsevier
2010
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| Online Access: |
Check full text
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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. |
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| Keywords: |
non-parametric method, quantile regression, natural cubic splines, Bayesian method |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
4 |
| Start Page: |
1138 |
| End Page: |
1150 |