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Let be the characteristic function, defined as the Fourier transform of the probability density function
using Fourier transform parameters
,
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(1) |
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(2) |
The cumulants are then defined by
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(3) |
(Abramowitz and Stegun 1972, p. 928). Taking the Maclaurin series gives
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(4) |
where are raw moments, so
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(5) |
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(6) |
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(7) |
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(8) |
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(9) |
These transformations can be given by CumulantToRaw[n] in the Mathematica application package mathStatica.
In terms of the central moments ,
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(10) |
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(11) |
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(12) |
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(13) |
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(14) |
where is the mean and
is the variance. These transformations can be given by CumulantToCentral[n].
Multivariate cumulants can be expressed in terms of raw moments, e.g.,
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(15) |
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(16) |
and central moments, e.g.,
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(17) |
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(18) |
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(19) |
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(20) |
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(21) |
using CumulantToRaw[m, n, ...
] and CumulantToCentral[
m, n, ...
], respectively.
The k-statistics are unbiased estimators of the cumulants.
REFERENCES:
Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th printing. New York: Dover, p. 928, 1972.
Kenney, J. F. and Keeping, E. S. "Cumulants and the Cumulant-Generating Function," "Additive Property of Cumulants," and "Sheppard's Correction." §4.10-4.12 in Mathematics of Statistics, Pt. 2, 2nd ed. Princeton, NJ: Van Nostrand, pp. 77-82, 1951.
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