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Re: st: ML regression
At 12:05 PM 6/1/2007, [email protected] wrote:
Thanks--I will look into those options. However, as far as I
know, imputation does not recover parameter estimates (and
SEs) as well as does ML (or FIML) estimation.
Best,
J.
You might want to take a look at the multiple imputation FAQ page at
http://www.stat.psu.edu/~jls/mifaq.html
Some excerpts:
Is MI the only principled way to handle missing data?
MI is not the only principled method for handling missing values, nor
is it necessarily the best for any given problem. In some cases, good
estimates can be obtained through weighted estimation procedures. In
fully parametric models, maximum-likelihood estimates can often be
calculated directly from the incomplete data by specialized numerical
methods, such as the EM algorithm. Those procedures may be somewhat
more efficient than MI because they involve no simulation. Given
sufficient time and resources, one could perhaps derive a better
statistical procedure than MI for any particular problem. In
real-life applications, however, where missing data are nuisance
rather than a the primary focus, an easy, approximate solution with
good properties can be preferable to one that is more efficient but
problem-specific and complicated to implement.
Is multiple imputation like EM?
MI bears a close resemblance to the EM algorithm and other
computational methods for calculating maximum-likelihood estimates
based on the observed data alone. These methods summarize a
likelihood function which has been averaged over a predictive
distribution for the missing values. MI performs this same type of
averaging by Monte Carlo rather than by numerical methods. In large
samples, when relevant aspects of the imputer's and analyst's models
agree, inferences obtained by MI with sufficiently many imputations
will be nearly the same as those obtained by direct maximization of
the likelihood.
Another good source:
http://www.multiple-imputation.com/
I'm hardly an expert on this, but my impression is that multiple
imputation isn't too bad compared to other methods - and if you want
to use Stata, I'm not sure what other options you have.
Official support for multiple imputation and/or other advanced
methods continues to be on my wish list for Stata 10.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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