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Re: st: Principal Components Analysis with count data


From   Evans Jadotte <evans.jadotte@uab.es>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Principal Components Analysis with count data
Date   Tue, 11 Aug 2009 09:09:09 +0200

Cameron McIntosh wrote:
Hi Jason, Adrian

You should also check out chapters 8 and 9 of:

Basilevsky, A. (1994). Statistical Factor Analysis and Related Methods: Theory and Applications. New York: Wiley.
Cam


----------------------------------------
From: kokootchke@hotmail.com
To: statalist@hsphsun2.harvard.edu
Subject: RE: st: Principal Components Analysis with count data
Date: Mon, 10 Aug 2009 20:12:39 -0400



Jason wrote:
As PCA is appropriate for continuous data. I am wondering if it is
appropriate for count data (i.e., highly skewed)? Can someone provide
advice, guidance or a resource in using PCA with count data?
Dear Jason,

I don't know much about this but a while ago I was looking for something similar and I came across this paper which helped me:

http://cosco.hiit.fi/search/MPCA/buntineDPCA.pdf

If that's not useful to you, it has a bunch of references in the back. Maybe those can help.

Best,
Adrian




Cheers,
Jason

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Hello Jason,

I think a straightforward way to deal with this issue is to apply a Multiple Correspondence Analysis (MCA) to your data. See Asselin (2002) for an application, and also reference therein.

Evans
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