Bayesian mixture of splines for spatially adaptive nonparametric regression

Sally A. Wood

Biometrika, 2002, vol. 89, issue 3, 513-528

Abstract: A Bayesian approach is presented for spatially adaptive nonparametric regression where the regression function is modelled as a mixture of splines. Each component spline in the mixture has associated with it a smoothing parameter which is defined over a local region of the covariate space. These local regions overlap such that individual data points may lie simultaneously in multiple regions. Consequently each component spline has attached to it a weight at each point of the covariate space and, by allowing the weight of each component spline to vary across the covariate space, a spatially adaptive estimate of the regression function is obtained. The number of mixing components is chosen using a modification of the Bayesian information criteria. We study the procedure analytically and show by simulation that it compares favourably to three competing techniques. These techniques are the Bayesian regression splines estimator of Smith & Kohn (1996), the hybrid adaptive spline estimator of Luo & Wahba (1997) and the automatic Bayesian curve fitting estimator of Denison et al. (1998). The methodology is illustrated by modelling global air temperature anomalies. All the computations are carried out efficiently using Markov chain Monte Carlo. Copyright Biometrika Trust 2002, Oxford University Press.

Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (20)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:89:y:2002:i:3:p:513-528

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().