Definitions
from The American Heritage® Dictionary of the English Language, 5th Edition.
- noun A quantity indicating how sharply a probability distribution function increases and decreases around the distribution's mean.
from Wiktionary, Creative Commons Attribution/Share-Alike License.
- noun statistics A measure of "peakedness" of a
probability distribution , defined as the fourthcumulant divided by the square of thevariance of the probability distribution.
Etymologies
from The American Heritage® Dictionary of the English Language, 4th Edition
from Wiktionary, Creative Commons Attribution/Share-Alike License
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Examples
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Moreover, the central limit theorem of probability theory does not apply in this context because empirical evidence shows that a constant standard deviation is an inaccurate measure of investment risk, due to the fact that investment performance, is typically skewed and exhibits kurtosis.
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I presume there was minimal kurtosis in these distributions.
Seven-Year WMAP Results: No, They're NOT Anomalies | Universe Today 2010
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Moreover, the central limit theorem of probability theory does not apply in this context because empirical evidence shows that a constant standard deviation is an inaccurate measure of investment risk, due to the fact that investment performance, is typically skewed and exhibits kurtosis.
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There is a kurtosis measure, a fourth moment as standard deviation is a second moment, for this variation.
A Dark, Misleading Force Sean 2007
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Normally they keep the tech-talk to minimum, but this one had a few eye-glazers: “Probability density function” and “variance and the kurtosis of the distribution changes”
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I would guess that a chi square goodness of fit test or a kurtosis/skewness test for normality would not eliminate a Poisson and/or a normal distribution as applying here without the sinusoidal correction.
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Or traditional bell curves do not apply because of non-normal distributions were distribution shapes tested for skew, kurtosis, etc?
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The variates not the observations have the same mean, variance, skew, kurtosis, etc.
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Further to #36: Thinking about this some more, the options point implies that we are interested not just in the mean and variance of the distribution but also the “tails” — skewness, kurtosis etc.
Gerry Browning: Numerical Climate Models « Climate Audit 2006
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The shape of the curve is very important kurtosis.
Whitfield subCommittee II: Mann under fire « Climate Audit 2006
MaryW commented on the word kurtosis
David M. Lane et al., Introduction to Statistics (I don't see the date), p. 669
April 7, 2018