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Bayesian nonparametric quantile regression using splines

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 ...

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Publication status:
Published

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Publisher copy:
10.1016/j.csda.2009.09.004

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Institution:
University of Oxford
Division:
MSD
Department:
Experimental Psychology
Role:
Author
Journal:
COMPUTATIONAL STATISTICS and DATA ANALYSIS
Volume:
54
Issue:
4
Pages:
1138-1150
Publication date:
2010-04-01
DOI:
ISSN:
0167-9473
Source identifiers:
457549
Language:
English
Pubs id:
pubs:457549
UUID:
uuid:361b6add-04d4-4123-b2e6-66779e98971a
Local pid:
pubs:457549
Deposit date:
2014-05-13

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