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The Economist reports that superforecasters are clever (with a good mental attitude), but not necessarily geniuses. It reports on the treasure trove of data coming from The Good Judgment Project, showing that accurately selected amateur forecasters (and the confidence they had in their forecasts) were often more accurately tuned than experts. [1]
In such a case, each predictor can be converted into a standard score, or z-score, so that all the predictors have a mean of zero and a standard deviation of one. With this method of unit-weighted regression, the variate is a sum of the z -scores (e.g., Dawes, 1979; Bobko, Roth, & Buster, 2007).
In this case, a perfect forecast results in a forecast skill metric of zero, and skill score value of 1.0. A forecast with equal skill to the reference forecast would have a skill score of 0.0, and a forecast which is less skillful than the reference forecast would have unbounded negative skill score values. [4] [5]
Confidence bands can be constructed around estimates of the empirical distribution function.Simple theory allows the construction of point-wise confidence intervals, but it is also possible to construct a simultaneous confidence band for the cumulative distribution function as a whole by inverting the Kolmogorov-Smirnov test, or by using non-parametric likelihood methods.
The Framingham Risk Score is a sex-specific algorithm used to estimate the 10-year cardiovascular risk of an individual. The Framingham Risk Score was first developed based on data obtained from the Framingham Heart Study, to estimate the 10-year risk of developing coronary heart disease. [1]
Bidding farewell to the mythical Dragon, the world welcomes the Year of the Snake on January 29 — the first day of the Lunar New Year. For those who celebrate this ancient festival, starting the ...
The event chart of Super Bowl 59, using the game’s kickoff time. Horary charts, which give quick answers to yes or no questions — think of them as astrological oracles. The birth charts of ...
Conformal prediction (CP) is a machine learning framework for uncertainty quantification that produces statistically valid prediction regions (prediction intervals) for any underlying point predictor (whether statistical, machine, or deep learning) only assuming exchangeability of the data. CP works by computing nonconformity scores on ...