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Is that a fancy way of saying that any negative signal was isolated from the rest of the signal, inverted, and added back in? That cannot be what it sounds like.

]]>Between your two posts I did a google search on the site and the first one I went to (which may have been the #1 hit), was the one in post 25. I like the way you start it:

I’ve already received my first condescending comments from the dendro world about the mysteries of standardization. Just to pre-empt some further pontification presuming that I know nothing about these mysteries, I’m posting up some notes that I wrote in 2004 on standardization – which was what I would have been working on had people just accepted the defects in the Mannian multiproxy articles at the time.

The trouble with having posted several thousand blogs is that people not regulars don’t know what’s been already discussed here. Hence the value of searching before complaining.

]]>It does indeed.

Also, FWIW, it seems to me that some of the non-normal distributions that we’ve seen model better as inverse gamma distributions than as log normal.

]]>but I suggest you at least try these options (with these data, if they become available, or with any other dataset) so you can be sure you are comparing “apples and apples” and see if your results match the original results/figures being discussed in your posts. In this case, I can assure you that all the options used by Esper can be performed/replicated in 5 minutes using ARSTAN.

Please see this CA post http://www.climateaudit.org/?p=2435 for a very thorough reconciliation of ARSTAN with my R emulation as it then stood.

The reconciliation presentated some interesting numerical analysis issues which do not resolve clearly in favor of ARSTAN even given their own assumptions. For some trees in the cana036 dataset (interestingly also used in Melvin 2007), I got *exact* replication using nls. In some cases, ARSTAN quit too soon and I could get a neg exp fit that ARSTAN missed. In some cases, whether or not you got a fit depended on your starting point. And most remarkably, the first core fit by ARSTAN was always fit wrong.

Another advantage of using R is that I can write routines to do entire networks in a few minutes. ARSTAN requires manual handling of the data. It’s 25 year old programming for the most part and very cumbersome for the simple tasks undertaken.

Plus, like Roman, my interest is statistical. What statistical interpretation can be placed on dendro recipes? As I’ve mentioned elsewhere, I view their methods as “artesanal” – not “wrong”, but done for practical purposes with little understanding of statistical practice. I don’t say this to deprecate things, because the problems are interesting and difficult when you combine crossed random effects, heteroskedasticity, nonlinearity, non-normality, autocorrelation,…

]]>Aside from anything else, at the time that I started analysing dendro functions, RCS was a ARSTAN option anyway.

]]>Interesting folks

]]>Thanks, Steve. That looks like a Gamma density function. It also appears that the fit might be more appropriately done after transforming the rings with logs as well.

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