Is the Cumulant Generating Function correctly defined?

AI Thread Summary
The discussion centers on the definition and properties of the Cumulant Generating Function (CGF), K(t), expressed as a series involving derivatives of the moment generating function, M(t). It establishes that K(t) is the natural logarithm of M(t) and derives relationships between K(t) and expected values of random variables. The participants confirm that K'(0) equals the first cumulant, while K''(0) represents the variance, linking these to the moments of the distribution. A correction is noted regarding the definition of K(t), emphasizing that K_n should be evaluated at zero. The accuracy of the CGF's formulation and its implications in statistical analysis are affirmed.
donutmax
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Cumulative generating function is
K(t)=K_1(t)t+K_2(t)\frac{t^2}{2!}+K_3(t)\frac{t^3}{3!}+...
where
K_{n}(t)=K^{(n)}(t)

Now
K(t)=ln M(t)=ln E(e^{ty})=ln E(f(0)+f'(0)\frac {t}{1!}+f''(0)\frac{t^2}{2!}+...)=ln E(1+\frac{t}{1!}y+\frac{t^2}{2!} y^2+...)=ln [1+\frac{t}{1!} E(Y)+\frac{t^2}{2!} E(Y^2)+...]=ln [1+\frac{t}{1!}\mu'_1+\frac{t^2}{2!}\mu'_2+...]
where \mu'_n=E(Y^n)
=>K(0)=ln1=0

Also
K'(t)=\frac{1}{M(t)}M'(t)
where
M(0)=1; M'(t)=\mu'_1+\frac{t}{1}\mu'_2+\frac{t^2}{2!}\mu'_3+...
=>M'(0)=\mu'_1

In fact
M^{(n)}(0)=\mu'_n

So
K'(0)=\frac{\mu'_1}{1}=\mu'_1

Furthermore
K''(t)=\frac{M''(t)M(t)-[M'(t)]^2}{[M(t)]^2}
=>K''(0)=\frac{\mu'_2*1-(\mu'_1)^2}{1^2}=\mu'_2-(\mu'_1)^2=E(Y^2)-[E(Y)]^2=\sigma^2

Is this correct?
 
Last edited:
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Correction:
K(t) is:
K(t)=K_1t+K_2\frac{t^2}{2!}+...
where
K_n=K^{(n)}(0)
 
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