The cumulant generating function Kx (0) of the random variable X is defined by Kx (0) log E(ex), the logarithm of the moment generating function of X. If the latter is finite in a neigh- bourhood of the origin, then Kx has a convergent Taylor expansion: 1 Kx (0) => kn (X)on n=1 and kn (X) is called the nth cumulant (or semi-invariant) of X. (a) Express k₁ (X), k₂(X), and k3(X) in terms of the moments of X. (b) If X and Y are independent random variables, show that kn (X+Y) = kn(X) + kn (Y). =

A First Course in Probability (10th Edition)
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The cumulant generating function Kx (0) of the random variable X is defined by Kx (0)
log E(ex), the logarithm of the moment generating function of X. If the latter is finite in a neigh-
bourhood of the origin, then Kx has a convergent Taylor expansion:
1
Kx (0) => kn (X)on
n!
n=1
and kn (X) is called the nth cumulant (or semi-invariant) of X.
(a) Express k₁ (X), k₂(X), and k3(X) in terms of the moments of X.
(b) If X and Y are independent random variables, show that kn (X+Y) = kn(X) + kn (Y).
=
Transcribed Image Text:The cumulant generating function Kx (0) of the random variable X is defined by Kx (0) log E(ex), the logarithm of the moment generating function of X. If the latter is finite in a neigh- bourhood of the origin, then Kx has a convergent Taylor expansion: 1 Kx (0) => kn (X)on n! n=1 and kn (X) is called the nth cumulant (or semi-invariant) of X. (a) Express k₁ (X), k₂(X), and k3(X) in terms of the moments of X. (b) If X and Y are independent random variables, show that kn (X+Y) = kn(X) + kn (Y). =
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