Tag Archives: brown

Conflict and Confidence: MBH99

Here’s a first attempt at applying the techniques of Brown and Sundberg 1987 to MBH99. The results shown here are very experimental, as I’m learning the techniques, but the results appear very intriguing and to hold some possibility for linking temperature reconstructions to known statistical methodologies – something that seems more scientifically useful than “PR […]

Brown and Sundberg: "Confidence and conflict in multivariate calibration" #1

Introduction If one is to advance in the statistical analysis of temperature reconstructions, let alone climate reconstructions – and let’s take improving the quality of the data as the obvious priority – Task One in my opinion is to place the ad hoc Team procedures used in reconstructions in a statistical framework known off the […]

Calibration in the Mann et al 2007 Network Revisited

In a post a few months ago, I discussed MBH99 proxies (and similar points will doubtless apply to the other overlapping series) from the point of view of the elementary calibration diagram of Draper and Smith 1981 (page 49), an older version of a standard text. Nothing exotic. One of the problems that arose in […]

MBH99 and Proxy Calibration

UC and Hu McCulloch have been carrying on a very illuminating discussion of statistical issues relating to calibration , with UC, in particular, drawing attention to the approach of Brown (1982) towards establishing confidence intervals in calibration problems. In order to apply statistical theory of regression , you have to regress the effect Y against […]

UC on CCE

I then compared verification statistics for the different reconstructions as shown below. OLS yielded much the “best” fit in the calibration period, but the worst fit in the verification period. If OLS is equivalent to ICE, it actually finds the best fit (minimizes calibration residuals), and in proxy-temperature case makes the most obvious overfit. Let […]

Multivariate Calibration

In calibration problem we have accurately known data values (X) and a responses to those values (Y). Responses are scaled and contaminated by noise (E), but easier to obtain. Given the calibration data (X,Y), we want to estimate new data values (X’) when we observe response Y’. Using Brown’s (Brown 1982) notation, we have a […]