While I’m often described as a “statistician”, as that’s a word that people understand (or think that they understand), I think of what I do more as “data analysis”. Academic statisticians are interested in different sorts of things than interest me. I have some styles, habits and practices for approaching new data sets, but they […]
In a story featured on the cover of Nature, Eric J. Steig, David P. Schneider, Scott D. Rutherford, Michael E. Mann, Josefino C. Comiso and Drew T. Shindell report to have found “significant warming” that “extends well beyond the Antarctic Peninsula to cover most of West Antarctica, an area of warming much larger than previously […]
CA reader hfl, who cited Buell’s documentation of the dependence of principal component patterns on shapes, has sent me a scanned pdf version now available here. It concludes by observing that analyses that fail to consider this phenomenon (and there is ample evidence that Steig et al falls into this category) “may well be scientific […]
Nicolas Nierenberg has taken a look here at Gavin Schmidt auditing of McKitrick and Michaels. He previously reported here on the analysis: Anyway I have written an analysis of spatial autocorrelation as it relates to S09 and MM07. My conclusion is that the primary result in MM07 was not affected by spatial autocorrelation, which is […]
Some Japanese articles have been in the news recently. CA readers will be interested in the fact that CA was cited (thanks to a CA reader for the heads up). Here’s a graphic from their SI showing differences between Gaspé versions. As CA readers know, similar discrepancies occur for bristlecones between Ababneh and Graybill or […]
Yesterday, I showed an interesting comparison between the 3 Steig eigenvectors and “Chladni patterns” generated by performing principal components on a grid of spatially autocorrelated sites on a disk. Today I’ll show a similar analysis, but this time using a random sample of points from actual Antarctica. The results are pretty interesting, to say the […]
Last year, I did a few posts connecting spatial autocorrelation to something as mundane as the Stahle/SWM tree ring network. In the process, I observed something that I found quite interesting – that principal components applied to geometric shapes with spatially autocorrelated series generated Chladni patterns, familiar from violins and sounds. The Antarctica vortex represents […]
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 […]
In case you’re wondering where we’ve been, the story is at Anthony Watts’ site. Anthony reports the resuscitation here. Thanks, Anthony. Will chat tomorrow.
“Noisy” covariance matrices have been discussed here on many occasions in a variety of contexts, largely because the underlying strategy of Mannian methods is to calculate the covariance of everything to everything else and then calculate verification stats using methods that ignore the data mining that effectively takes place with huge covariance matrices. Steig et […]
Climate Audit 