**From:** A.J. Rossini (*rossini@blindglobe.net*)

**Date:** Thu 13 May 2004 - 03:23:05 EST

**Next message:**A.J. Rossini: "Re: [R] missing values imputation"**Previous message:**Prof Brian Ripley: "Re: [R] missing values imputation"**In reply to:**Ted Harding: "Re: [R] missing values imputation"**Next in thread:**Ted Harding: "Re: [R] missing values imputation"**Reply:**Ted Harding: "Re: [R] missing values imputation"

Message-id: <85y8nxo8zq.fsf@servant.blindglobe.net>

(Ted Harding) <Ted.Harding@nessie.mcc.ac.uk> writes:

*> 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?
*

Thanks, Ted :-) -- to extend it a bit, one can imagine the use of

approximate solutions to the 2 steps (simulation methods to get

expected values, similar range of approaches for the maximization) and

get a general (but possibly not robust) computational solution for

the parametric problem. Just plug in a formula for the likelihood and

the sufficient statistics...

Of course, thousands of papers have been written on these variations

(likelihood, specific implementations of the E and M steps).

best,

-tony

-- rossini@u.washington.edu http://www.analytics.washington.edu/ Biomedical and Health Informatics University of Washington Biostatistics, SCHARP/HVTN Fred Hutchinson Cancer Research Center UW (Tu/Th/F): 206-616-7630 FAX=206-543-3461 | Voicemail is unreliable FHCRC (M/W): 206-667-7025 FAX=206-667-4812 | use EmailCONFIDENTIALITY NOTICE: This e-mail message and any attachme...{{dropped}}

______________________________________________ R-help@stat.math.ethz.ch mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

**Next message:**A.J. Rossini: "Re: [R] missing values imputation"**Previous message:**Prof Brian Ripley: "Re: [R] missing values imputation"**In reply to:**Ted Harding: "Re: [R] missing values imputation"**Next in thread:**Ted Harding: "Re: [R] missing values imputation"**Reply:**Ted Harding: "Re: [R] missing values imputation"

*
This archive was generated by hypermail 2.1.3
: Mon 31 May 2004 - 23:05:09 EST
*