On Wed, Oct 15, 2008 at 9:19 AM, David Cournapeau <[EMAIL PROTECTED]>wrote:
> On Wed, Oct 15, 2008 at 11:45 PM, Travis E. Oliphant > <[EMAIL PROTECTED]> wrote: > > Gabriel Gellner wrote: > >> Some colleagues noticed that var uses biased formula's by default in > numpy, > >> searching for the reason only brought up: > >> > >> > http://article.gmane.org/gmane.comp.python.numeric.general/12438/match=var+bias > >> > >> which I totally agree with, but there was no response? Any reason for > this? > > I will try to respond to this as it was me who made the change. I think > > there have been responses, but I think I've preferred to stay quiet > > rather than feed a flame war. Ultimately, it is a matter of preference > > and I don't think there would be equal weights given to all the > > arguments surrounding the decision by everybody. > > > > I will attempt to articulate my reasons: dividing by n is the maximum > > likelihood estimator of variance and I prefer that justification more > > than the "un-biased" justification for a default (especially given that > > bias is just one part of the "error" in an estimator). Having every > > package that computes the mean return the "un-biased" estimate gives it > > more cultural weight than than the concept deserves, I think. Any > > surprise that is created by the different default should be mitigated by > > the fact that it's an opportunity to learn something about what you are > > doing. Here is a paper I wrote on the subject that you might find > > useful: > > > > > https://contentdm.lib.byu.edu/cdm4/item_viewer.php?CISOROOT=/EER&CISOPTR=134&CISOBOX=1&REC=1 > > (Hopefully, they will resolve a link problem at the above site soon, but > > you can read the abstract). > > Yes, I hope too, I would be happy to read the article. > > On the limit of unbiasdness, the following document mentions an > example (in a different context than variance estimation): > > > http://www.stat.columbia.edu/~gelman/research/published/badbayesresponsemain.pdf<http://www.stat.columbia.edu/%7Egelman/research/published/badbayesresponsemain.pdf> > > AFAIK, even statisticians who consider themselves as "mostly > frequentist" (if that makes any sense) do not advocate unbiasdness as Frequently frequentist? Chuck
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