Statistics - Moment Generating Functions

In summary, the moment generating function (m.g.f.) of a random variable X is defined as the expected value of etX, where t is a variable and X is a random variable. The series expansion of etX can be obtained by differentiating the m.g.f. r times with respect to t and then setting t = 0. This will give the rth moment about the origin μr'. This method can be used to find the m.g.f. of a uniform distribution, which is given by 1 + (t/2!) + (t2/3!) + (t3/4!) + ..., by differentiating the series r times and setting t = 0. The resulting expression is μr' =
  • #1
mliuzzolino
58
0

Homework Statement



The moment generating function (m.g.f.) of a random variable X is defined as the Expected value of etX:

M(t) = E(etX).

The series expansion of etX is:

etX = 1 + tX + (t2X2)/(2!) + (t3X3)/(3!) + ...

Hence,

M(t) = E(etx) = 1 + tμ1' + (t2μ2')/2! + (t3μ3')/3! + ...


If we differentiate M(t) r times with respect to t and then set t = 0 we shall therefore obtain the rth moment about the origin μr'.

For example, the m.g.f. of the uniform distribution is:
M(t) = E(etX) = [itex] \int_0^1 e^{tx} [/itex] dx = [itex] \dfrac{1}{t}e^{tx} [/itex] |[itex]_0^1 = \dfrac{1}{t}(e^t - 1) [/itex]

= [itex] 1 + \dfrac{t}{2!} + \dfrac{t^2}{3!} + \dfrac{t^3}{4!} + [/itex] ...

Differentiate this series r times and then set t = 0. Show that μr' = [itex] \dfrac{1}{r + 1} [/itex].

Homework Equations





The Attempt at a Solution



I tried to do this by differentiating a few times to find a pattern:


[itex] 1 + \dfrac{t}{2!} + \dfrac{t^2}{3!} + \dfrac{t^3}{4!} + \dfrac{t^4}{5!} [/itex] ...

Differentiate once:
[itex] \dfrac{1}{2!} + \dfrac{2t}{3!} + \dfrac{3t^2}{4!} + \dfrac{4t^3}{5!} [/itex] ...

Differentiate twice:
[itex] \dfrac{2}{3!} + \dfrac{6t}{4!} + \dfrac{12t^2}{5!} [/itex] ...

Differentiate thrice:
[itex] \dfrac{6}{4!} + \dfrac{24t}{5!} [/itex] ...

Since t will be set to zero, only the first terms of each differentiation "survive:"

[itex] \dfrac{1}{2!}, \dfrac{2}{3!}, \dfrac{6}{4!}, \dfrac{24}{5!}, ... [/itex]

The pattern from this I noticed was: [itex] \dfrac{r!}{(r + 1)!} [/itex], where (r+1)! = (r+1)r!

So, [itex] \dfrac{r!}{(r+1)r!} = \dfrac{1}{r+1} [/itex].

This seems correct as I've obviously arrived at the correct expression, but I am wondering if I have approached this correctly and if there might have been any errors in my thought process.

I am also a little bit lost conceptually as to why t is set equal to 0. What does setting t = 0 signify?

I'm self-studying this topic and I'd really like to have a full understanding of what's going on rather than a superficial understanding where I'm just accepting that it says set t = 0. I'd appreciate any amount of elucidation!

Thanks!
 
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  • #2
your method is good. And as for why t is set to zero... I just think of it as a useful 'trick'. I guess you can think of it like you are just considering the zeroth order term of the series. If you want to look into more detail, then maybe look at the wiki page on "characteristic function (probability theory)" Which I think is the nice generalisation of the generating function.
 
  • #3
mliuzzolino said:

Homework Statement



The moment generating function (m.g.f.) of a random variable X is defined as the Expected value of etX:

M(t) = E(etX).

The series expansion of etX is:

etX = 1 + tX + (t2X2)/(2!) + (t3X3)/(3!) + ...

Hence,

M(t) = E(etx) = 1 + tμ1' + (t2μ2')/2! + (t3μ3')/3! + ...


If we differentiate M(t) r times with respect to t and then set t = 0 we shall therefore obtain the rth moment about the origin μr'.

For example, the m.g.f. of the uniform distribution is:
M(t) = E(etX) = [itex] \int_0^1 e^{tx} [/itex] dx = [itex] \dfrac{1}{t}e^{tx} [/itex] |[itex]_0^1 = \dfrac{1}{t}(e^t - 1) [/itex]

= [itex] 1 + \dfrac{t}{2!} + \dfrac{t^2}{3!} + \dfrac{t^3}{4!} + [/itex] ...

Differentiate this series r times and then set t = 0. Show that μr' = [itex] \dfrac{1}{r + 1} [/itex].

Homework Equations





The Attempt at a Solution



I tried to do this by differentiating a few times to find a pattern:


[itex] 1 + \dfrac{t}{2!} + \dfrac{t^2}{3!} + \dfrac{t^3}{4!} + \dfrac{t^4}{5!} [/itex] ...

Differentiate once:
[itex] \dfrac{1}{2!} + \dfrac{2t}{3!} + \dfrac{3t^2}{4!} + \dfrac{4t^3}{5!} [/itex] ...

Differentiate twice:
[itex] \dfrac{2}{3!} + \dfrac{6t}{4!} + \dfrac{12t^2}{5!} [/itex] ...

Differentiate thrice:
[itex] \dfrac{6}{4!} + \dfrac{24t}{5!} [/itex] ...

Since t will be set to zero, only the first terms of each differentiation "survive:"

[itex] \dfrac{1}{2!}, \dfrac{2}{3!}, \dfrac{6}{4!}, \dfrac{24}{5!}, ... [/itex]

The pattern from this I noticed was: [itex] \dfrac{r!}{(r + 1)!} [/itex], where (r+1)! = (r+1)r!

So, [itex] \dfrac{r!}{(r+1)r!} = \dfrac{1}{r+1} [/itex].

This seems correct as I've obviously arrived at the correct expression, but I am wondering if I have approached this correctly and if there might have been any errors in my thought process.

I am also a little bit lost conceptually as to why t is set equal to 0. What does setting t = 0 signify?

I'm self-studying this topic and I'd really like to have a full understanding of what's going on rather than a superficial understanding where I'm just accepting that it says set t = 0. I'd appreciate any amount of elucidation!

Thanks!

You are trying to get the Maclaurin expansion, which reads as
[tex] f(t) = f(0) + t f'(0) + \frac{t^2}{2!} f''(0) + \cdots + \frac{t^n}{n!} f^{(n)}(0) + \cdots [/tex]
The coefficients are obtained by getting higher derivatives and evaluating them at ##t=0##.

Another way to see this for your specific case is to look at the higher derivatives of the different terms. We have
[tex] \left(\frac{d}{dt}\right)^r t^k = \left\{
\begin{array}{cl} 0 &, \; r > k\\
r! &, \; r = k \\
r! t^{k-r}&, \; r < k
\end{array}
\right. [/tex]
When we set t = 0 we are just picking out the rth term.
 
  • #4
That makes a lot of sense. I appreciate it both of you!
 

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

A moment generating function (MGF) is a mathematical function that provides a way to uniquely characterize a probability distribution, similar to how a fingerprint uniquely identifies a person. It is defined as the expected value of e^tx, where t is a real number and x is a random variable. The MGF can be used to derive the moments of a distribution, such as the mean and variance.

2. How is the MGF related to the probability distribution function (PDF)?

The MGF and PDF are closely related, but they serve different purposes. The MGF is a mathematical tool for characterizing a probability distribution, while the PDF gives the actual probabilities of different outcomes. In fact, the MGF can be used to derive the PDF by taking the derivatives of the MGF with respect to t and evaluating it at t=0.

3. What is the importance of MGF in statistics?

The MGF is important in statistics because it allows us to calculate the moments of a distribution, which are useful in understanding its properties. For example, the first moment is the mean, the second moment is the variance, and so on. The MGF also plays a crucial role in the Central Limit Theorem, which states that the sum of independent random variables will tend towards a normal distribution as the number of variables increases, as long as their MGFs exist.

4. Can the MGF be used to find the distribution of a sum of random variables?

Yes, the MGF can be used to find the distribution of a sum of independent random variables. This is because the MGF of a sum of independent random variables is equal to the product of their individual MGFs. This property of MGFs is particularly useful in solving problems involving sums of random variables, such as in hypothesis testing and confidence interval calculations.

5. Are there any limitations of using MGFs in statistics?

One limitation of MGFs is that they may not exist for all probability distributions. In some cases, the MGF may be undefined or infinite, making it impossible to use this method to find the moments of the distribution. Additionally, the calculation of MGFs can be complicated and time-consuming, especially for complex distributions. In these cases, alternative methods such as characteristic functions may be used instead.

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