Adding two distributions with same moment generating function

In summary, the question is about finding the result of adding two distributions with the same moment generating function. The attempt at a solution incorrectly suggests that the solution would be (1/3 + 2/3et)2, but the correct approach is to use the relationship between moment generating functions and the fact that the sum of independent random variables has an mgf equal to the product of their individual mgfs. This results in an mgf of (1/3 + 2/3*exp(t))^2 for X+Y and 2*(1/3 + 2/3*exp(t)) for the sum of their distributions.
  • #1
trojansc82
60
0

Homework Statement



I wanted to know what the result would be if you added two distributions with the same moment generating function.

For example, what would the result be of:

Mx(t) + My(t) if Mx(t) = (1/3 + 2/3et) and My(t) = (1/3 + 2/3et)

Homework Equations





The Attempt at a Solution



Would the solution be (1/3 + 2/3et)2?
 
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  • #2
trojansc82 said:

Homework Statement



I wanted to know what the result would be if you added two distributions with the same moment generating function.

For example, what would the result be of:

Mx(t) + My(t) if Mx(t) = (1/3 + 2/3et) and My(t) = (1/3 + 2/3et)

Homework Equations




The Attempt at a Solution



Would the solution be (1/3 + 2/3et)2?

No. Denoting the mgf of X by [itex]M_X(t)[/itex] we have the relationship
[tex]M_{a+bX}(t) = E(e^{t(a+bX)}) = E(e^{at}e^{btX})=e^{at}E(e^{btX})
=e^{at}M_X(bt)[/tex]

You are asking about the mgf of X + X = 2X. So using the above with a = 0 and b = 2 gives [itex]M_{2X}(t) = M_X(2t)[/itex].
 
  • #3
trojansc82 said:

Homework Statement



I wanted to know what the result would be if you added two distributions with the same moment generating function.

For example, what would the result be of:

Mx(t) + My(t) if Mx(t) = (1/3 + 2/3et) and My(t) = (1/3 + 2/3et)

Homework Equations





The Attempt at a Solution



Would the solution be (1/3 + 2/3et)2?

There is a difference between adding distributions (or mgf's) and adding random variables. Which do you mean? If you want to find the MGF of X+Y, and assuming X,Y are independent, you get (1/3 + 2/3*exp(t))^2. If you want to find the MGF of the sum of the distributions of X and Y, you get 2*(1/3 + 2/3*exp(t)), but this thing does not really have much meaning.

RGV
 

Related to Adding two distributions with same moment generating function

1. What is the moment generating function?

The moment generating function (MGF) is a mathematical function that characterizes the properties of a probability distribution. 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 calculate moments of a distribution, such as the mean and variance.

2. Can two distributions with the same MGF be added together?

Yes, two distributions with the same moment generating function can be added together. This is because the MGF uniquely determines the entire distribution, so two distributions with the same MGF must be the same distribution.

3. How do you add two distributions with the same MGF?

To add two distributions with the same MGF, you simply add the corresponding random variables from each distribution. For example, if X and Y are two random variables with the same MGF, the resulting distribution of X+Y will have the same MGF.

4. Are there any limitations to adding distributions with the same MGF?

Yes, there are limitations to adding distributions with the same MGF. This method only works for distributions that are independent and identically distributed (i.i.d.). If the distributions are not i.i.d., the resulting distribution may not have the same MGF.

5. What are the practical applications of adding distributions with the same MGF?

Adding distributions with the same MGF can be useful in situations where you need to combine two or more random variables. For example, in statistics, this method can be used to combine multiple samples from the same population to estimate the population mean or variance. It can also be applied in fields such as finance and engineering to model complex systems with multiple random variables.

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