Re: [R] missing values imputation

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From: A.J. Rossini (rossini@blindglobe.net)
Date: Thu 13 May 2004 - 03:34:30 EST


Message-id: <85lljxo8gp.fsf@servant.blindglobe.net>


Picky, picky.

Details are in the eyes of the beholder.

Prof Brian Ripley <ripley@stats.ox.ac.uk> writes:

> That's not an algorithm. It is a recipe for deriving an algorithm.
>
> algorithm - A detailed sequence of actions to perform to accomplish some
> task. Named after an Iranian mathematician, Al-Khawarizmi.
>
> Technically, an algorithm must reach a result after a finite number of
> steps, thus ruling out brute force search methods for certain problems,
> though some might claim that brute force search was also a valid (generic)
> algorithm. The term is also used loosely for any sequence of actions
> (which may or may not terminate).
>
> Paul E. Black's Dictionary of Algorithms, Data Structures, and Problems.
>
> On Wed, 12 May 2004 Ted.Harding@nessie.mcc.ac.uk wrote:
>
>> 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.
>>
>>
>> --------------------------------------------------------------------
>> E-Mail: (Ted Harding) <Ted.Harding@nessie.mcc.ac.uk>
>> Fax-to-email: +44 (0)870 167 1972
>> Date: 12-May-04 Time: 17:57:53
>> ------------------------------ XFMail ------------------------------
>>
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>>
>>
>
> --
> Brian D. Ripley, ripley@stats.ox.ac.uk
> Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
> University of Oxford, Tel: +44 1865 272861 (self)
> 1 South Parks Road, +44 1865 272866 (PA)
> Oxford OX1 3TG, UK Fax: +44 1865 272595
>
> ______________________________________________
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>

-- 
rossini@u.washington.edu            http://www.analytics.washington.edu/ 
Biomedical and Health Informatics   University of Washington
Biostatistics, SCHARP/HVTN          Fred Hutchinson Cancer Research Center
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