Probability/Moment Generating Function

In summary, the conversation discusses finding the PDF of Y=eX, where X follows a Normal distribution. The attempt at a solution involves using the fact that fy(y) = |d/dy g-1(y)| fx(g-1(y)) and the moment generating function, ψY(t)=E(etY). However, it is shown that the integral for ψY(t) does not converge, indicating that the moment generating function for Y does not exist. The next step is to analyze the behavior of e^{ty} f(y) for y -> +∞ to prove this.
  • #1
tiger2030
22
0

Homework Statement


Let X ~ Normal(μ,σ2). Define Y=eX.
a) Find the PDF of Y.
b) Show that the moment generating function of Y doesn't exist.

Homework Equations

The Attempt at a Solution


For part a, I used the fact that fy(y) = |d/dy g-1(y)| fx(g-1(y)). Therefore I got that fy(y)= (1/y)(1/√(2piσ2)e-(ln(y)-μ)2/2σ2

Then for b), I used ψY(t)=E(etY)=∫etyfy(y)dy. When I plug in fy(y) I get a function that is nonlinear and too complicated to integrate. If someone could give me a hint on the next step it would be greatly appreciated.
 
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  • #2
You need to analyze the behavior of ##e^{ty} f(y)## for ##y \to + \infty## in order to show that the integral does not converge. You do not need to actually compute the integral to do that.
 
  • #3
Where do I start in showing that (1/y)ety-ln(y)2/(2σ2)+2μln(y)/(2σ2) does not converge?
 

Related to Probability/Moment Generating Function

What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of different outcomes occurring in a random experiment. It assigns a probability to each possible outcome, and the total probability of all outcomes must equal 1.

What is a moment generating function?

A moment generating function is a mathematical function that provides information about the moments 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. It is used to find the mean, variance, and other moments of a distribution.

How is a probability distribution related to a moment generating function?

A moment generating function is closely related to a probability distribution, as it allows for the calculation of the moments of the distribution. By taking derivatives of the moment generating function, one can find the moments of the distribution, such as the mean and variance.

What is the difference between a discrete and continuous probability distribution?

A discrete probability distribution is one in which the possible outcomes are countable, such as rolling a dice or flipping a coin. A continuous probability distribution is one in which the possible outcomes are not countable, such as the height of a person or the amount of rainfall in a day.

How is the moment generating function used in statistics?

The moment generating function is used in statistics to find the moments of a probability distribution, which can provide important information about the distribution, such as the mean and variance. It can also be used to prove the Central Limit Theorem, which states that the sum of a large number of independent random variables will have a normal distribution.

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