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Date: 7-3-2021
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Date: 8-3-2021
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Given a random variable and a probability density function , if there exists an such that
(1) |
for , where denotes the expectation value of , then is called the moment-generating function.
For a continuous distribution,
(2) |
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(3) |
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(4) |
where is the th raw moment.
For independent and , the moment-generating function satisfies
(5) |
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(6) |
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(7) |
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(8) |
If is differentiable at zero, then the th moments about the origin are given by
(9) |
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(10) |
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(11) |
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(12) |
The mean and variance are therefore
(13) |
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(14) |
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(15) |
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(16) |
It is also true that
(17) |
where and is the th raw moment.
It is sometimes simpler to work with the logarithm of the moment-generating function, which is also called the cumulant-generating function, and is defined by
(18) |
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(19) |
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(20) |
But , so
(21) |
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(22) |
REFERENCES:
Kenney, J. F. and Keeping, E. S. "Moment-Generating and Characteristic Functions," "Some Examples of Moment-Generating Functions," and "Uniqueness Theorem for Characteristic Functions." §4.6-4.8 in Mathematics of Statistics, Pt. 2, 2nd ed. Princeton, NJ: Van Nostrand, pp. 72-77, 1951.
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