Bayesian backfitting (with comments and a rejoinder by the authors
By Trevor J. Hastie, Robert Tibshirani. 2000
167 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/51ca761a702a54cb74f4b0098174fa65330dce02
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Thomas Kneib
- Stefan Lang
- Ludwig Fahrmeir
- Andreas Brezger
- C. Biller
- Edward I. George
- Robert E. McCulloch
- Hugh A. Chipman
In Statistics and Probability
- Thomas Kneib
- Stefan Lang
- Ludwig Fahrmeir
- Andreas Brezger
- C. Biller
- N. Klein
- Robert E. McCulloch
- Hugh A. Chipman
Citations in the corpus
- Bayesian varying-coefficient models using adaptive regression splines (2001, methodology): “Based on smoothness priors, Hastie and Tibshirani (2000) develop an efficient block move Gibbs sampler for a Bayesian version of smoothing splines in Gaussian additive models.”
- BART: Bayesian Additive Regression Trees (2008, methodology): “…m successive draws of ( T j , M j ) conditionally on ( T ( j ) , M ( j ) , σ ): j = 1 , . . . , m , followed by a draw of σ from the full conditional: Hastie & Tibshirani (2000) considered a similar application of the Gibbs sampler for posterior sampling for additive and generalized additive…”
- PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE (2004, background): “These are random walk priors or more generally autoregressive priors (see Fahrmeir and Lang (2001a) and Fahrmeir and Lang (2001b)), Bayesian P-splines (Lang and Brezger (2003)) and Bayesian smoothing splines (Hastie and Tibshirani (2000)).”