Re: SHORT COURSE ON MISSING DATA, APRIL 23-24

[Phillip Good]

As I suspected you are unable to offer a single justification for your actions just as Rumsfield was unable to offer a single justification for his.  Given your recklessness, may I recommend "Common Errors in Statistics (and How to Avoid Them),"  Wiley, 2003.

 

Phillip Good

[EMAIL PROTECTED] wrote:

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 wi! ll 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
> >
> > .
> > .
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> >
> > 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> >
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>
> .
> .
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>
> 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
>
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Instructions for joining and leaving this list, remarks about the
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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

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