Tag Archives: principal components

Why did Steig use a cut-off parameter of k=3?

A question that Jean S inquired about before we were so rudely interrupted. The expanation in Steig et al was: Principal component analysis of the weather station data produces results similar to those of the satellite data analysis, yielding three separable principal components. We therefore used the RegEM algorithm with a cut-off parameter k=3…. A […]

"Mannian" PCA Revisited #1

One of Hansen’s pit bulls, Tamino, has re-visited Mannian principal components. Tamino is a bright person, whose remarks are all too often marred by the pit bull persona that he has unfortunately chosen to adopt. His choice of topic is a curious one, as he ends up re-opening a few old scabs, none of which […]

Regression and Varimax Rotation

I’ve been reading through some articles on altitudinal reconstructions by Rob Wilson and other Luckman students. The studies all follow a similar strategy as Wilson et al 2007 – principal components analysis; truncation to eigenvalues 1, varimax rotation and regression. It’s pretty obvious that these operations are all linear and if the linear algebra were […]

More on PCs

DF criticized my post on principal components yesterday as follows: Most of your figures for conventional PC analysis are misleading. You are comparing PCA1 to mean as if PCA1 has an intrinsically meaningful scale, when it does not. If you rescaled your comparison plots so that PCA1 and the mean had the same variance, then […]

Some Principal Components Illustrations

TCO has been pressing about the exact impact of various properties of the MBH PC methodology, asking some "elementary" questions about PC impact. Some readers have criticized him for in effect asking for a tutorial on PC methods. However, if someone asked: where can I find an article showing the statistical properties of PC methods […]

Principal Components applied to Red Noise

We’ve obviously spent quite a bit of time analyzing the effect of the weird and incorrect MBH principal components method on red noise series. We’ve not argued that doing the principal components calculation correctly necessarily results in a meaningful index, only that doing it incorrectly cannot result in a meaningful index. One thing that I […]


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