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

[Phillip Good]

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.

 

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 illus! trated 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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