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Bayesian Backfitting

Trevor Hastie and Robert Tibshirani
Statistical Science
Vol. 15, No. 3 (Aug., 2000), pp. 196-213
Stable URL: http://www.jstor.org/stable/2676659
Page Count: 18
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Bayesian Backfitting
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Abstract

We propose general procedures for posterior sampling from additive and generalized additive models. The procedure is a stochastic generalization of the well-known backfitting algorithm for fitting additive models. One chooses a linear operator ("smoother") for each predictor, and the algorithm requires only the application of the operator and its square root. The procedure is general and modular, and we describe its application to nonparametric, semiparametric and mixed models.

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