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| Abstract |
 Search result page 19
| Title: | An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers |
| Author: | Garrick Wallstrom |
| Abstract: | BARS (DiMatteo, Genovese, and Kass 2001) uses the powerful reversible-jump MCMC engine to perform spline-based generalized nonparametric regression. It has been shown to work well in terms of having small mean-squared error in many examples (smaller than known competitors), as well as producing visually-appealing fits that are smooth (filtering out high-frequency noise) while adapting to sudden changes (retaining high-frequency signal). However, BARS is computationally intensive. The original implementation in S was too slow to be practical in certain situations, and was found to handle some data sets incorrectly. We have implemented BARS in C for the normal and Poisson cases, the latter being important in neurophysiological and other point-process applications. The C implementation includes all needed subroutines for fitting Poisson regression, manipulating B-splines (using code created by Bates and Venables), and finding starting values for Poisson regression (using code for density estimation created by Kooperberg). The code utilizes only freely-available external libraries (LAPACK and BLAS) and is otherwise self-contained. We have also provided wrappers so that BARS can be used easily within S or R. |
| Journal: | Journal of Statistical Software |
| Issn: | 15487660 |
| EIssn: | |
| Year: | 2007 |
| Volume: | 26 |
| Issue: | 1 |
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| Key words | curve-ļ¬tting ; free-knot splines ; nonparametric regression ; peri-stimulus time histogram ; Poisson process |
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