Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection-Reference-Cited by-同舟云学术

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Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection

Published:2009-11-21 Issue:2 Volume:20 Page:203-219
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Fahrmeir Ludwig,Kneib Thomas,Konrath Susanne

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s11222-009-9158-3.pdf

Reference58 articles.

1. Baladandayuthapani, V., Mallick, B.K., Carroll, R.J.: Spatially adaptive Bayesian penalized regression splines (P-splines). J. Comput. Graph. Stat. 14, 378–394 (2005)

2. Besag, J., York, J., Mollié, A.: Bayesian image restoration with two applications in spatial statistics. Ann. Inst. Stat. Math. 43, 1–59 (1991)

3. Bigelow, J.L., Dunson, D.B.: Bayesian adaptive regression splines for hierarchical data. Biometrics 63, 724–732 (2007)

4. Bondell, H.D., Reich, B.J.: Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR. Biometrics 64, 115–123 (2008)

5. Brezger, A., Lang, S.: Generalized additive regression based on Bayesian P-splines. Comput. Stat. Data Anal. 50, 967–991 (2006)

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