Folks: If generalities -- with the attendant risk of occasional specific caveats and violations -- can be tolerated, then George Box's (paraphrased) comments of circa 40-50 years ago seem apropos: why do statisticians obsess over normality, to which most analyses -- i.e. inference (especially from balanced designs)-- are robust, when lack of independence of the observations is the violation of assumptions that can reek the greatest havoc on the statistical analysis?
Time series analysis and mixed effects models are among modern statistics ways of dealing with such lack of indepndence, btw. Cheers, Bert Bert Gunter Genentech Nonclinical Biostatistics -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of David Scott Sent: Wednesday, October 28, 2009 2:09 PM To: Kjetil Halvorsen Cc: Karl Ove Hufthammer; [email protected] Subject: Re: [R] Non-normal residuals. Kjetil Halvorsen wrote: > On Wed, Oct 28, 2009 at 7:25 AM, David Scott <[email protected]> wrote: >> Karl Ove Hufthammer wrote: >>> On Tue, 27 Oct 2009 18:06:02 -0400 Ben Bolker <[email protected]> wrote: >>>> If transforming your data brings you closer to satisfying >>>> the assumptions of your analytic methods and having a sensible >>>> analysis, then that's good. If it makes things worse, that's bad. >>>> Other choices, depending on the situation, include robust methods >>>> (for "outlier" problems); generalized linear models etc. (for >>>> discrete data from standard distributions); models using t- instead >>>> of normally distributed residuals; >>> I have sometimes wondered about this: Which functions/packages do you use >>> to fit a (perhaps just a simple linear) model with t-distributed residuals >>> (or residuals of a different distribution)? >>> >> Package sn has this facility I believe. > > Yes, for independent data, but for time series??? > > Kjetil > > No, not for time series---I was responding to "fit a (perhaps just a simple linear) model with t-distributed residuals" David _________________________________________________________________ David Scott Department of Statistics The University of Auckland, PB 92019 Auckland 1142, NEW ZEALAND Phone: +64 9 923 5055, or +64 9 373 7599 ext 85055 Email: [email protected], Fax: +64 9 373 7018 Director of Consulting, Department of Statistics ______________________________________________ [email protected] 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. ______________________________________________ [email protected] 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.
