On Fri, 2008-05-23 at 14:42 -0700, David Hewitt wrote: > > Kingsford Jones wrote: > > > > I don't think it is useful to put this in a Bayesian vs. frequentist > > framework. Burnham and Anderson write: > > > > "AIC can be justified as Bayesian using a > > 'savvy' prior on models that is a function of sample size and the number > > of > > model parameters Furthermore, BIC can be derived as a non-Bayesian result. > > Therefore, arguments about using AIC versus BIC for model selection cannot > > be > > from a Bayes versus frequentist perspective." > > > > see: > > http://www2.fmg.uva.nl/modelselection/presentations/AWMS2004-Burnham-paper.pdf > > > > Model selection doesn't reduce to AIC vs. BIC, or to Bayesian vs. > frequentist. AIC and BIC are only two approaches for model selection, after > all. That was part of my main point. Nonetheless, the fact remains that > Bayesian methods differ from "pure" likelihood methods, in principle and in > practice. If you're going to use BIC, how will you choose your priors?
BIC assumes a unit reference prior. That is, a prior containing information equivalent to one observation. > It's > a practical issue. EJW has done a lot of work on model selection and I > thought his papers were a good intro to the variety of approaches. > > > > >> All that said, since you're dealing with random effects, Bayesian > >> approaches > >> do appear to have the upper hand at present, and a shift in that > >> direction > >> may be warranted. > > > > Can you expound on the last paragraph? > > > > Others on the list are far better positioned than I to expound, but as a > lurker in stats journals I see a lot more work on model selection methods > for models with random effects in a Bayesian context. For instance, type > "random effects model selection" into Google and almost all the first 20 > results are Bayesian. David Anderson told me personally that he thinks I-T > methods (AICc) are really struggling with random effects. I don't honestly > know how the various packages in R calculate the AIC values for models with > random effects (of course, you can look and see), but I'd guess it's > something you have to be rather careful about. I still need to read Pinheiro > and Bates, obviously. > > ----- > David Hewitt > Research Fishery Biologist > USGS Klamath Falls Field Station (USA) -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320 Goddard Building (8) T: +61 7 3365 2506 http://www.uq.edu.au/~uqsblomb email: S.Blomberg1_at_uq.edu.au Policies: 1. I will NOT analyse your data for you. 2. Your deadline is your problem. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
