Proving variance with moment generating functions

In summary, the moment generating function can be used to show that Var(X) is equal to the second derivative of the natural logarithm of the generating function evaluated at t=0. Additionally, the formula for calculating Var(X) is given as the second moment minus the square of the first moment, which can be expressed in terms of the generating function and its derivatives. Plugging in t=0 simplifies the expression.
  • #1
TelusPig
15
0
Moment generating functions:
How can I show that [itex]Var(X)=\frac{d^2}{dt^2}ln M_X(t)\big |_{t=0}[/itex]

Recall:
[itex]M_X(t)=E(e^{tx})=\int_{-\infty}^{\infty}e^{tx}f(x)dx[/itex]

[itex]E(X^n)=\frac{d^n}{dt^n}M_X(t)\big |_{t=0}[/itex]

[itex]Var(X)=E(X^2)-[E(X)]^2=E[(X-E(X))^2][/itex]
------------
I tried just applying the equation given but I don't know what to do with the log of this general integral?
[itex]\frac{d^2}{dt^2}ln M_X(t)=\frac{d^2}{dt^2}ln \left( \int_{-\infty}^{\infty}e^{tx}f(x)dx \right)[/itex]
 
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  • #2
If you were asked the more general question of calculating
[tex] \frac{d^2}{dt^2} \ln\left( f(t) \right) [/tex]
What's the first thing you would do?
 
  • #3
It would be for 1st derivative: [itex]\frac{1}{f(t)}*f'(t)[/itex]
then differentiate again for the 2nd derivative, that would be:

[itex]\frac{f''(t)f(t)-(f'(t))^2}{(f(t))^2}[/itex]
 
Last edited:
  • #4
So once you do that you've expressed everything in terms of the generating function and its derivatives without any logarithms involved.
 
  • #5
Does that mean it's [itex]\frac{M_X''(t)M_X(t)-(M_X'(t))^2}{(M_X(t))^2}[/itex]

Do I have to then substitute each M(t), M'(t), M''(t) with it's integral definition then? and somehow simplify that big mess o.o?
 
  • #6
No, you need to plug in t=0!
 
  • #7
Office_Shredder said:
No, you need to plug in t=0!
OH obviously! LOL omg, I can't believe I didn't see that and thought I had to do a bunch of integrals ._. Thanks! :D
 

1. What is a moment generating function (MGF)?

A moment generating function is a mathematical function that generates the moments of a probability distribution. It is a useful tool in statistics for determining the properties of a probability distribution, such as its mean, variance, and higher moments.

2. How is the MGF used to prove variance?

The MGF is used to prove variance by evaluating the second derivative of the MGF at zero. This value is equal to the variance of the probability distribution. If the second derivative exists at zero, then the variance exists and can be calculated using this method.

3. Can the MGF be used to prove variance for any probability distribution?

Yes, the MGF can be used to prove variance for any probability distribution, as long as the MGF exists and has a finite second derivative at zero. However, there may be other methods that are more efficient or suitable for specific distributions.

4. What are the advantages of using the MGF to prove variance?

One advantage of using the MGF to prove variance is that it is a general method that can be applied to any probability distribution. It also provides a straightforward and systematic approach to calculating the variance by evaluating the second derivative at a single point.

5. Are there any limitations to using the MGF to prove variance?

One limitation of using the MGF to prove variance is that it may not always be easy to find the MGF of a given probability distribution. Additionally, the MGF may not exist or have a finite second derivative at zero for some distributions, making this method ineffective for proving variance in those cases.

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