Cumming, J. A. & Wooff, D. A. 2007. Dimension reduction via principal
variables. Computational Statistics & Data Analysis 52(1): 550-565.
Extends from GP McCabe's 1984 paper. Also check out the references therein.
On 27/10/2010 13:23, K F Pearce wrote:
> Hello all,
> I'd appreciate it if anyone has any references on the following or can offer their views:
> I am very familiar with using PCA in the usual way i.e. using a set of p correlated variables we generate a set of p uncorrelated PCs where each PC is a linear combination of these variables....we can choose to retain the first m PCs....then, say, plot these PCs' scores against each other.
> Now my question is....I have discussed, informally, in the past that PCs can be used also to reduce a number of variables. Say we had p potential correlated variables....we could conduct a PCA on these variables....and then, from each of the subsequent *important* PCs, choose those variables with the largest (positive or negative) coefficients.....these variables are taken as being the 'important ones' and can be used in further analysis.
> Does this seem OK?
> If so, can this technique be used when the p variables are (i) potential independent variables or (ii) potential response variables.
> Many thanks for your views on this,
> Kind Regards,
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