Mgf of a random variable with added constant

In summary, the author is trying to calculate the moment generating function of a r.v. Y=Z+c, but is having difficulty. They try to use an exponential function but it turns out to be incorrect. They then find a different way to calculate the function and are successful.
  • #1
WantToBeSmart
10
0
Hey,

I have a pdf of a random variable Z given. I am being asked to calculate what the moment generating function of a r.v Y= Z + c will be where c is a constant in ℝ

I tried to calculate it in the following way:

[tex] \int^∞_0 e^{(z+c)t} f(z+c)dz[/tex] where [tex] f(z) [/tex] is an exponential pdf with parameter λ.

but it proved to be an unsuccessful method. Could anyone please show me the right direction? I know I could use Jacobian transformation but I'm sure there is an easier method.

Thank you in advance!
 
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  • #2
I wouldn't even mess around with the integral. Here is something I would try:

[itex] Y = Z + c [/itex] where [itex] Z ~ exp(\lambda) [/itex] and c is a constant. Then,

[itex]E[e^{tY}] = E[e^{t(Z+c)}] [/itex]

Now do you see what you might be able to do?
 
  • #3
Robert1986 said:
I wouldn't even mess around with the integral. Here is something I would try:

[itex] Y = Z + c [/itex] where [itex] Z ~ exp(\lambda) [/itex] and c is a constant. Then,

[itex]E[e^{tY}] = E[e^{t(Z+c)}] [/itex]

Now do you see what you might be able to do?

I think it definitely solves this problem! Now I can proceed with the rest of the exercise. Thank you Robert!
 
  • #4
You're most certainly welcome.

As a side note, this sort of thing is a rather valuable technique in prob/stat. That is, if you want to know about a certain RV, or a certain expectation, lots of times it is best to work it into some form you already know.
 
  • #5
WantToBeSmart said:
I think it definitely solves this problem! Now I can proceed with the rest of the exercise. Thank you Robert!

Of course, you would have gotten the same result had you used the correct f(z) dz in your integration, instead of your _incorrect_ f(z+c) dz.

RGV
 
  • #6
Ray Vickson said:
Of course, you would have gotten the same result had you used the correct f(z) dz in your integration, instead of your _incorrect_ f(z+c) dz.

RGV

Checked that and it was another mistake I was making. Thank you for pointing this out!
 

1. What is the formula for finding the MGF of a random variable with added constant?

The MGF (moment generating function) of a random variable X with added constant c is given by M(t) = E[e^(t(X+c))] = e^(tc) * M(t), where M(t) is the MGF of X.

2. How is the MGF of a random variable with added constant different from the MGF of the original variable?

The MGF of a random variable X with added constant c is essentially the same as the MGF of X, but with an additional factor of e^(tc). This means that the MGF of X with added constant will have a different shape and may have different properties such as different moments and cumulants.

3. What is the significance of the MGF of a random variable with added constant?

The MGF of a random variable with added constant is a mathematical tool that allows us to calculate the moments and cumulants of the variable. It is also useful for finding the distribution of a sum of independent random variables, as the MGF of the sum is equal to the product of the MGFs of the individual variables.

4. Can the MGF of a random variable with added constant be used to find the distribution of the variable?

Yes, the MGF of a random variable with added constant can be used to find the distribution of the variable. This is because the MGF uniquely determines the distribution of a random variable. However, in some cases, it may be difficult or impossible to find the inverse of the MGF, making it challenging to determine the distribution.

5. How can the MGF of a random variable with added constant be used in practical applications?

The MGF of a random variable with added constant can be used in various applications, such as finance, statistics, and engineering. For example, in finance, it can be used to calculate the risk and return of a portfolio of assets, and in statistics, it can be used to estimate the parameters of a distribution. Additionally, the MGF can also be used to test hypotheses about the distribution of a random variable.

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