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Spatially adaptive smoothing splines

Abstract:
We use a reproducing kernel Hilbert space representation to derive the smoothing spline solution when the smoothness penalty is a function λ(t) of the design space t, thereby allowing the model to adapt to various degrees of smoothness in the structure of the data. We propose a convenient form for the smoothness penalty function and discuss computational algorithms for automatic curve fitting using a generalised crossvalidation measure. © 2006 Biometrika Trust.
Publication status:
Published

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Publisher copy:
10.1093/biomet/93.1.113

Authors


Pintore, A More by this author
Speckman, P More by this author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics, Clinical Medicine
Journal:
BIOMETRIKA
Volume:
93
Issue:
1
Pages:
113-125
Publication date:
2006-03-05
DOI:
EISSN:
1464-3510
ISSN:
0006-3444
URN:
uuid:373a482b-1bcd-4477-8260-9d986ef0688e
Source identifiers:
97546
Local pid:
pubs:97546

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