[Impute] RE: Impute Digest, Vol 47, Issue 1

[Gregorich, Steven]

Hi, David.

It sounds like you are talking about a zero-inflated model, e.g., a 
zero-inflated Poisson (ZIP) or zero-inflated negative 
binomial (ZINB). You could also fit a zero-inflated normal model (ZIN; Joe 
Schafer and a colleague wrote a paper 
about that model in the late 1990's; they called it a 'two-part model'). I 
never studied the ZIN model closely, but 
presumably the normal part of the model would allow negative predicted values, 
which I would not be happy with 
in your application.

You can fit a 2-level multilevel ZIP, ZIN, ZINB, etc in PROC NLMIXED and you 
can probably find related SAS
code in the SAS-L archives; especially posts by Dale McLerran (sp?).

HTH

Steve 
________________________________________

Message: 1
Date: Fri, 4 Sep 2009 14:25:33 -0400
From: David Judkins <judki...@westat.com>
Subject: [Impute] Robustness of Multi-Level Modeling Software
To: "impute@lists.utsouthwestern.edu"
        <impute@lists.utsouthwestern.edu>
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This is not an imputation question, but I don't know of a list serve for 
complex modeling questions.  Maybe one of you will be able to help.

Consider a mixed binary-normal distribution that results in a large point mass 
on the edge of an otherwise more-or-less normal distribution.  An example is 
number of alcoholic drinks per day.  Cigarettes per day is another example. Or 
the number of questions reading questions answered correctly on a sample that 
contains a large number of children who can't read at all.  The child reading 
example is my real concern because the children come grouped by school.

Anyone know of robustness studies of MLwin, HLM, Mixed, MPLUS, et cetera to 
this radical departure from normality?  I have heard it asserted that 
school-level departures from normality are more of a concern than student-level 
departures, but is this too much of a departure?


David Judkins
Senior Statistician
Westat
1650 Research Boulevard
Rockville, MD 20850
(301) 315-5970
davidjudk...@westat.com
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