Moment Generating Function - Integration Help

In summary, we have a probability theory problem where (X,Y) is distributed with a joint density of f(x,y)=1/4(1+xy(x^2-y^2)) if abs(x)≤1, abs(y)≤1; 0 otherwise. We need to find the MGF of (X,Y) and determine if X and Y are independent. If not, we need to find the covariance. To find the MGF, we set up the integral ∫∫e^(sx+ty)f(x,y)dx dy with both integrals from -1 to 1. We can use the fact that e^{sx+ty} = e^{sx}e^{ty} and that e
  • #1
ARLM
4
0
I am working on a probabilty theory problem:

Let (X,Y) be distributed with joint density
f(x,y)=(1/4)(1+xy(x^2-y^2)) if abs(x)≤1, abs(y)≤1; 0 otherwise

Find the MGF of (X,Y). Are X,Y independent? If not, find covariance.

I have set up the integral to find the mgf

∫∫e^(sx+ty)f(x,y)dx dy
with both integrals from -1 to 1.

I am having trouble integrating this though in order to move on with the problem. I began to try integration by parts and I do not think that is the best route but have no other ideas.
If anyone can help, I would greatly appreciate it!
 
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  • #2
ARLM said:
I am working on a probabilty theory problem:

Let (X,Y) be distributed with joint density
f(x,y)=1/4(1+xy(x^2-y^2)) if abs(x)≤1, abs(y)≤1; 0 otherwise

Find the MGF of (X,Y). Are X,Y independent? If not, find covariance.

I have set up the integral to find the mgf

∫∫e^(sx+ty)f(x,y)dx dy
with both integrals from -1 to 1.

I am having trouble integrating this though in order to move on with the problem. I began to try integration by parts and I do not think that is the best route but have no other ideas.
If anyone can help, I would greatly appreciate it!

Please use parentheses. Do you mean
[tex] f(x,y) = \frac{1}{4}(1 + xy(x^2 - y^2)), [/tex]
or do you mean
[tex] f(x,y) = \frac{1}{4(1 + xy(x^2 - y^2))}?[/tex]
If you mean the first one, just write (1/4)(...) to make it clear; if you mean the second one, write 1/(4(...)).
 
  • #3
Yes, I mean the first one! Thank you.
 
  • #4
ARLM said:
Yes, I mean the first one! Thank you.

OK. So given the conditions on |x| and |y|, what are the ranges of x and y themselves?

Since you have a 2-variable integration, you can integrate first over y (for any fixed x), then integrate the result over x; or you can do it in the other order. Is that what you have tried to do?

You really need to supply more details, since we have no way of helping if we do not know where you are stuck.
 
  • #5
I set up the integral
[itex]\frac{1}{4}[/itex][itex]\int[/itex][itex]\int[/itex]e[itex]^{sx+ty}[/itex](1+xy(x[itex]^{2}[/itex]+y[itex]^{2}[/itex]))dx dy

Both integrals are from -1 to 1 (I don't know how to show that)

I tried distributing xy and using integration by parts with u = 1+(x^3)y +x(y^3) and dv= e^(sx+ty) but not sure that this is the right idea because it would take numerous integrations before reducing to one function and then I would have to do it again for the second integral.

Other than that, I haven't tried anything.
 
  • #6
ARLM said:
I set up the integral
[itex]\frac{1}{4}[/itex][itex]\int[/itex][itex]\int[/itex]e[itex]^{sx+ty}[/itex](1+xy(x[itex]^{2}[/itex]+y[itex]^{2}[/itex]))dx dy

Both integrals are from -1 to 1 (I don't know how to show that)

The [itex]x^2 - y^2[/itex] in the original seems to have turned into [itex]x^2 + y^2[/itex].

I tried distributing xy and using integration by parts with u = 1+(x^3)y +x(y^3) and dv= e^(sx+ty) but not sure that this is the right idea because it would take numerous integrations before reducing to one function and then I would have to do it again for the second integral.

Other than that, I haven't tried anything.

Use the fact that [itex]e^{sx+ty} = e^{sx}e^{ty}[/itex], and that [itex]e^{sx}[/itex] does not depend on y to get
[tex]
\frac14 \int_{-1}^1 e^{sx}\left(\int_{-1}^1 e^{ty}(1 + xy(x^2 - y^2))\,\mathrm{d}y\right)\,\mathrm{d}x
[/tex]

Start by working out [itex]\int_{-1}^1 xe^{kx}dx[/itex], [itex]\int_{-1}^1 x^2e^{kx}dx[/itex], and [itex]\int_{-1}^1 x^3e^{kx}dx[/itex]. You're going to need these repeatedly, so you may as well do them once and then write in the values as needed.

You may also want to note that [itex]e^k - e^{-k} = 2\sinh k[/itex] and that [itex]e^k + e^{-k} = 2\cosh k[/itex]
 
  • #7
Ok, thanks!
 

1. What is a moment generating function?

A moment generating function (MGF) is a mathematical function that is used to describe the probability distribution of a random variable. It is defined as the expected value of e^(tx), where t is a real number and x is the random variable.

2. How is a moment generating function used in statistics?

MGFs are commonly used in statistics to calculate moments of a probability distribution, such as the mean, variance, and higher moments. They also play a key role in determining the distribution of a sum of independent random variables.

3. How do you calculate the moment generating function?

The moment generating function can be calculated by taking the expected value of e^(tx), where t is a real number. This involves integrating the probability density function (PDF) of the random variable with respect to x. The result is a function of t, which is the MGF.

4. What is the purpose of integrating the moment generating function?

Integrating the MGF allows us to find the moments of a probability distribution. These moments can then be used to calculate important statistics, such as the mean and variance. Integration also allows us to determine the distribution of a sum of independent random variables.

5. Are there any limitations to using moment generating functions?

There are a few limitations to using MGFs. They may not exist for all probability distributions, and even when they do exist, they may not be easy to calculate. Additionally, the MGF may not be defined for all values of t, which can limit its usefulness in some situations.

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