Re: [R] missing values imputation

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From: Ted Harding (Ted.Harding@nessie.mcc.ac.uk)
Date: Thu 13 May 2004 - 02:57:53 EST


Message-id: <XFMail.040512175753.Ted.Harding@nessie.mcc.ac.uk>

On 12-May-04 Rolf Turner wrote:
> Anne Piotet wrote:
>
>> What R functionnalities are there to do missing values imputation
>> (substantial proportion of missing data)? I would prefer to use
>> maximum likelihood methods ; is the EM algorithm implemented? in
>> which package?
>
> The so-called ``EM algorithm'' is ***NOT*** an
> algorithm. It is a methodology or a unifying concept.
> It would be impossible to ``implement'' it. (Except
> possibly by means of some extremely advanced and
> sophisticated Artificial Intelligence software.)

Do we understand the same thing by "EM Algorithm"?

The one I'm thinking of -- formulated under that name by Dempster,
Laird and Rubin in 1977 ("Maximum likelihood estimation from incomplete
data via the EM algorithm", JRSS(B) 39, 1-38) -- is indeed an algorithm
in exactly the same sense as any iterative search for the maximum of a
function.

Essentially, in the context of data modelled by an underlying exponential
family distribution where there is incomplete information about the
values which have this distribution, it proceeds by

Start: Choose starting estimates for the parameters of the distribution
E: Using the current parameter values, compute the expected vaues
   of the sufficient statistics conditional on the observed information
M: Solve the maximum-likelihood equations (which are functions of the
   sufficient statistics) using the expected values computed in (E)
If sufficently converged, stop. Otherwise, make the current parameter
values equal to the values estimated in (M) and return to (E).

Algorithm, this, or not????

And where does "extremely advanced and sophisticated Artificial
Intelligence software" come into it? You can, in some cases, perform
the above EM algorithm by hand.

Which "EM Algorithm" are you thinking of?

Best wishes,
Ted.

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Date: 12-May-04 Time: 17:57:53
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