Our recent findings show that the equivalent local-level proxy noise variance is , or even more. This level of SNR appears unprecedented in the context of at least the past 2000 scientific publications. This anomalous finding can only be explained by modern anthropogenic forcing of subjective human-induced components into the research results. Unless urgent and strenuous mitigation actions are taken immediately, a whole branch of science will lose its credibility for decades ahead.

(Sorry, just trying to learn how to write like a pro scientist;) )

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proxy=A(local_temp+noise)

i.i.d (spatial and temporal) Gaussian noise , would explain the last figure, A is the unknown scale.

]]>Man that was a tough class. The third in his series is for radar, btw. ๐

Mark

]]>Sinan, in signal processing Decision Theory is known (for historical reasons) as Detection Theory. Much of the terminology comes from the radar engineering (think airplane in a radar). The null hypothesis is that the signal is absent from the data (noise only), and the probability of a Type I error (level of significance) is called “false alarm probability”๐

And to all nitpickers out there: I do understand that the *intended meaning* of Juckes’ “99.98% significant” is 1 minus the ordinary meaning of significant.

I should have dropped the last three sentences because this argument is incomplete. Instead, I should have added that finding a signal is neccassary but not sufficient. You still need to demonstrate that this model has some sort of validity.

]]>no one would make estimates from a regression equation unless he were convinced, either by theoretical argument or the statistical significance of b itself, that he knew the sign

That was 1969, but now we are much more advanced๐

]]>One should know (from physical considerations) if each proxy is responding positively or negatively to the temperature.

Williams (1969) A Note on Regression Methods in Calibration, Technometrics Vol. 11, No. 9:

]]>The fact that we need to know the sign of before choosing the estimator is only a minor objection to its use, since no one would make estimates from a regression equation unless he were convinced, either by theoretical argument or the statistical significance of b itself, that he knew the sign of .

Corrected my CVM computations and included past climate reconstruction using univariate classical calibration estimator. same url http://www.geocities.com/uc_edit/cvm.html

None of my errors affect my previously published (#66) results.

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