Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks
Regressions using variables categorized or listed numerically, like 1st one, 2nd one, etc. – such as...
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This pap...
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This pap...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
A Bayesian approach to nonparametric regression using Penalized splines (P-splines) is presented. Th...
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using...
An increasingly popular tool for nonparametric smoothing are penalized splines (P-splines) which use...
Methods for fitting survival regression models with a penalized smoothed hazard function have been r...
From simple NLMs to complex GLMMs, this book describes how to use the GUI for WinBUGS - BugsXLA - an...
WinBUGS is a program for Bayesian model fitting by Gibbs sampling. WinBUGS has limited facilities fo...
We describe and contrast several different bootstrap procedures for penalized spline smoothers. The ...
Kauermann G, Claeskens G, Opsomer JD. Bootstrapping for Penalized Spline Regression. JOURNAL OF COMP...
Penalized splines approach has very important applications in statistics. The idea is to fit the unk...
Regressions using variables categorized or listed numerically, like 1st one, 2nd one, etc. – such as...
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This pap...
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This pap...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed m...
A Bayesian approach to nonparametric regression using Penalized splines (P-splines) is presented. Th...
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using...
An increasingly popular tool for nonparametric smoothing are penalized splines (P-splines) which use...
Methods for fitting survival regression models with a penalized smoothed hazard function have been r...
From simple NLMs to complex GLMMs, this book describes how to use the GUI for WinBUGS - BugsXLA - an...
WinBUGS is a program for Bayesian model fitting by Gibbs sampling. WinBUGS has limited facilities fo...
We describe and contrast several different bootstrap procedures for penalized spline smoothers. The ...
Kauermann G, Claeskens G, Opsomer JD. Bootstrapping for Penalized Spline Regression. JOURNAL OF COMP...
Penalized splines approach has very important applications in statistics. The idea is to fit the unk...
Regressions using variables categorized or listed numerically, like 1st one, 2nd one, etc. – such as...
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This pap...
Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This pap...