The burden of proof remains on you [EMAIL PROTECTED] --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.
[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 <[EMAIL PROTECTED]>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
>
> .
> .
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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
problem of INAPPROPRIATE MESSAGES, and archives are available at:
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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
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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