In Stata you have -ice- and -hotdeck- procedures for missing data. The first
is based on Chained-Equations technique (CE), which is explained at
http://www.multiple-imputation.com/, the second is regression technique...
an interesting analysis is available in Efron, B. (1994). Missing Data,
Imputation, and the Bootstrap. Journal of the American Statistical
Association, 89, 463-478. A third technique based on EM algorithm (basically
Maximum Likelihood) and it uses Bayesian-principle is available in the
free-software NORM by J. Schafer [His book (1997) Analysis of Incomplete
Multivariate Data (1997) is a classic in the Multiple Imputation literature
as Rubin (1987). Multiple Imputation for Nonresponse in Surveys]. EM has
well-known asymptotic properties in compare with CE, but CE has good results
in Monte Carlo experiments. I suggest you NORM, because it is pretty easy to
use and it is very fast in compare with -ice-. If you can use SAS as well P.
Allison wrote a code that mimics NORM. See his webpage
http://www.ssc.upenn.edu/~allison/ and you can have an introductory lesson
with this paper: http://www.ssc.upenn.edu/~allison/MultInt99.pdf. Rodrigo.
----- Original Message -----
From: "Richard Williams" <[email protected]>
To: <[email protected]>
Sent: Tuesday, September 05, 2006 9:15 AM
Subject: Re: st: how to handle missing observations in a regression model
At 05:56 AM 9/5/2006, Joseph Coveney wrote:
>You can explore the behavior of this approach using -simulate- with a
>data-generating process that mimics what you expect prevails in your study.
>(This includes the mechanism of missingness.) A rudimentary example of
>this
>is shown below. It has 5% randomly missing in both predictors. The
>results
>indicate that for this approach, compared to just listwise deletion, there
One of the interesting points in Allison's Missing Data book is that,
of all the more or less traditional approaches to handling missing
data, listwise deletion tends to work as well or better as
anything. You have to go to the more recent and advanced techniques
if you want to do better.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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