Random Variables and Transformations

In summary, the solution to getting a region for y1, y2, and y3 would be different if the X vars were not iid.
  • #1
Artusartos
247
0
In the last question in this link:

http://pages.uoregon.edu/csinclai/teaching/Fall2009/files/hw8.pdf

1) I did not understand how they got the region for y1, y2, and y3...

2) How would the solution be different (or not possible) if X1, X2, and X3 were not iid?

Thanks in advance
 
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  • #2
Artusartos said:
1) I did not understand how they got the region for y1, y2, and y3...
Clearly each is >0. Y1 = X1/(X1+X2), with X1 and X2 both > 0. So X1 < X1+X2, so Y1 < 1. Y1 approaches 1 as X2 approaches zero. Etc.

The proof omits the derivation of the exp[-(y1...] term. If you dig into that you'll find it needed the joint pdfs of the X vars, and it assumes they're iid in the process.
 
  • #3
haruspex said:
Clearly each is >0. Y1 = X1/(X1+X2), with X1 and X2 both > 0. So X1 < X1+X2, so Y1 < 1. Y1 approaches 1 as X2 approaches zero. Etc.

The proof omits the derivation of the exp[-(y1...] term. If you dig into that you'll find it needed the joint pdfs of the X vars, and it assumes they're iid in the process.

Thanks a lot :)

I have one more question (if you don't mind)...

Let f(x1, x2, x3) = e-(x1+x2+x3), 0<x1,2,3<infinity, zero elsewhere be a joint pdf of X1, X2, X3.

Compute P(X1= X2< X3)

Determine the joint mgf.

So this is the integral that we need to integrate, right (for the mgf)?

[itex]\int_0^{\infty} \int_0^{\infty} \int_0^{\infty} e^{x_1(t_1-1)} e^{x_2(t_2-1)} e^{x_3(t_3-1)} dx_1 dx_2 dx_3 [/itex]

But I'm having some trouble with this, because this is what I get after integrating...

[itex]\frac{1}{t_1-1}e^{x_1(t_1-1)} |_0^{\infty} \frac{1}{t_2-1}e^{x_2(t_2-1)} |_0^{\infty} \frac{1}{t_3-1}e^{x_3(t_3-1)} |_0^{\infty}[/itex]...but if we plug in infinity to [itex]\frac{1}{t_1-1}e^{\infty(t_1-1)}[/itex]...don't we get infinity, since e increases forever?
 
  • #4
Artusartos said:
if we plug in infinity to [itex]\frac{1}{t_1-1}e^{\infty(t_1-1)}[/itex]...don't we get infinity, since e increases forever?
Not if t1 < ... what?
 
  • #5
haruspex said:
Not if t1 < ... what?

If t1<1...
 
  • #6
Artusartos said:
If t1<1...
Right. It's quite usual that the mgf would only be defined for a range of the parameter, such as |t| < 1. To make use of it, you only need all the derivatives to exist at 0.
 
  • #7
haruspex said:
Right. It's quite usual that the mgf would only be defined for a range of the parameter, such as |t| < 1. To make use of it, you only need all the derivatives to exist at 0.

Thanks...
 

Related to Random Variables and Transformations

1. What is a random variable?

A random variable is a variable that takes on different numerical values based on the outcome of a random event. It is used to model uncertainty or randomness in a system.

2. What are the types of random variables?

There are two types of random variables: discrete and continuous. Discrete random variables can only take on a finite or countably infinite number of values, while continuous random variables can take on any value within a given range.

3. What is a probability distribution?

A probability distribution is a function that assigns probabilities to each possible value of a random variable. It represents the likelihood of each outcome occurring in a random experiment or event.

4. What is a transformation of a random variable?

A transformation of a random variable is a mathematical function that is applied to a random variable to obtain a new random variable. It can be used to manipulate the original distribution of the variable to better fit a desired model or hypothesis.

5. How is the mean of a transformed random variable calculated?

The mean of a transformed random variable can be calculated by applying the transformation function to the original mean of the random variable. In other words, if X is a random variable with mean μ and Y is the transformed variable, then the mean of Y is E(Y) = f(μ), where f is the transformation function.

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