On 17 Mar 2004 at 8:19, Phillip Good wrote: > The burden of proof remains on you [EMAIL PROTECTED] --
Really? You are attacking a workhorse of statistics, but apparently you know something none other of us knows 1) - all opf this is misled, 2) you have a better method. Until you tell us about your better method I stay with models and with George Box' "All models are wrong, but some are usefull" > unless what > you intended to say was: > > As we know, > There are known knowns. > There are things we know we know. > We also know > There are known unknowns. > That is to say > We know there are some things > We do not know. > But there are also unknown unknowns, > The ones we don't know > We don't know. > You said that, not me. Kjetil > [EMAIL PROTECTED] wrote: > On 16 Mar 2004 at 12:26, Phillip Good wrote: > > > I was unaware that maximum likelihood had any desirable properties > > except in the case of normally-distributed random variables where > > the max likelihood approach leads to estimators that are desirable > > for entirely different reasons. > > > > Could you please explain what in your opinion is wrong with likelihood > methods, which in effect makes up the workhorse of todays applied > statistics, not only for normal models, but for instance in > generalized linear models and a lot of others? > > What is your opinion on the likelihood principle, as referenced in a > text I referenced in another letter today? > > Kjetil Halvorsen > > > Phillip Good > > > > Paul Allison wrote: > > On April 23-24, 2004, I will be offering a two-day course in > > Philadelphia on Missing Data . > > > > After reviewing the strengths and weaknesses of conventional > > methods, the course will focus two newer methods, maximum likelihood > > and multiple imputation, that have much better statistical > > properties. These new methods have been around for at least a > > decade, but have only become practical in the last few years with > > the introduction of widely available and user friendly software. > > What's remarkable is that these methods depend on less demanding > > assumptions than those required for conventional methods. At > > present, maximum likelihood is best suited for linear models or > > log-linear models for contingency tables. Multiple imputation, on > > the other hand, can be used for virtually any statistical problem. > > > > Multiple imputation will be illustrated with the new MI procedure in > > SAS. Maximum likelihood will be implemented with structural equation > > modeling software (either Amos or LISREL). > > > > The text for the course will be my "Missing Data" published by Sage > > in 2001. > > > > For complete details, go to www.ssc.upenn.edu/~allison > > > > . > > . > > ================================================================= > > Instructions for joining and leaving this list, remarks about the > > problem of INAPPROPRIATE MESSAGES, and archives are available at: . > > http://jse.stat.ncsu.edu/ . > > ================================================================= > > > > Phillip Good > > http.ms//www.statistician.usa > > "Never trust anything that can think for itself if you can't see > > where it keeps its brain." JKR > > > > Do you Yahoo!? > > Yahoo! Mail - More reliable, more storage, less spam > > > . > . > ================================================================= > Instructions for joining and leaving this list, remarks about the > problem of INAPPROPRIATE MESSAGES, and archives are available at: . > http://jse.stat.ncsu.edu/ . > ================================================================= > > Phillip Good > http.ms//www.statistician.usa > "Never trust anything that can think for itself if you can't see where > it keeps its brain." JKR > > Do you Yahoo!? > Yahoo! Mail - More reliable, more storage, less spam . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
