Hi everyone, I am wondering if there exists a stepwise regression function for the Bayesian regression model. I tried googling, but I couldn't find anything. I know "step" function exists for regular stepwise regression, but nothing for Bayes. Thanks -- View this message in context: http://www.nabble.com/Bayesian-regression-stepwise-function--tp26013725p26013725.html Sent from the R help mailing list archive at Nabble.com.
Allan.Y wrote:> > Hi everyone, > > I am wondering if there exists a stepwise regression function for the > Bayesian regression model. I tried googling, but I couldn't find > anything. I know "step" function exists for regular stepwise regression, > but nothing for Bayes. >Why? That seems so ... un-Bayesian ... -- View this message in context: http://www.nabble.com/Bayesian-regression-stepwise-function--tp26013725p26015081.html Sent from the R help mailing list archive at Nabble.com.
Frank E Harrell Jr <f.harrell at vanderbilt.edu> wrote>Ben Bolker wrote: >> >> >> Allan.Y wrote: >>> Hi everyone, >>> >>> I am wondering if there exists a stepwise regression function for the >>> Bayesian regression model. I tried googling, but I couldn't find >>> anything. I know "step" function exists for regular stepwise regression, >>> but nothing for Bayes. >>> >> >> Why? That seems so ... un-Bayesian ... > >Exactly. I hope it doesn't exist. The beauty of Bayes is shrinkage, >borrowing of information, and statement of results in an intuitive way.Yeah. Asking for stepwise in Bayesian analysis is like asking for some nuclear waste on your ice cream sundae. Peter Peter L. Flom, PhD Statistical Consultant Website: www DOT peterflomconsulting DOT com Writing; http://www.associatedcontent.com/user/582880/peter_flom.html Twitter: @peterflom
JLucke at ria.buffalo.edu
2009-Oct-23 18:31 UTC
[R] Bayesian regression stepwise function?
The BIC (Raftery) can be used for quasi-Bayesian model selection, but it's not stepwise. Ntzoufras shows how to use WinBUGS to conduct Bayesian model selection, but again it's not stepwise Ntzoufras, I. (2002), 'Gibbs variable selection using BUGS', Journal of Statistical Software 7(7), 1--19. Ntzoufras, I. (2009), Bayesian modeling using WinBUGS, Wiley, Hoboken, NJ. Raftery, A. E. (1995), 'Bayesian model selection in social research', Sociological Methodology 25, 111-163. "Allan.Y" <allany@cmu.edu> Sent by: r-help-bounces@r-project.org 10/22/2009 01:09 PM To r-help@r-project.org cc Subject [R] Bayesian regression stepwise function? Hi everyone, I am wondering if there exists a stepwise regression function for the Bayesian regression model. I tried googling, but I couldn't find anything. I know "step" function exists for regular stepwise regression, but nothing for Bayes. Thanks -- View this message in context: http://www.nabble.com/Bayesian-regression-stepwise-function--tp26013725p26013725.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]]
Ravi Varadhan <RVaradhan at jhmi.edu> wrote> >I have heard this (i.e. only head-to-head comparisons are valid) and various >other folklores about AIC and BIC based model selection before, including >one that these information criteria are only applicable for comparing two >nested models. > >Where has it been demonstrated that AIC/BIC cannot be used to find the best >subset, i.e. the subset that is closest to the "true" model (assuming that >true model is contained in the set of models considered, and that maximum >likelihood estimation is used for estimating parameters in the models)? > >I would greatly appreciate any reference that shows this. >Burnham and Anderson state a different result - not exactly opposite, but different - in that they recommend use of AICC to choose among several competing models. But defining 'best' is tricky. In most situations where there are many variables, each of several models will be almost equally good, and which is 'best' would vary if you took a different sample from the same population. Peter Peter L. Flom, PhD Statistical Consultant Website: www DOT peterflomconsulting DOT com Writing; http://www.associatedcontent.com/user/582880/peter_flom.html Twitter: @peterflom