How to find the expected value of a continuous variable with pdf fy= y^(-2)?

In summary, the conversation discusses finding the expected value of a continuous variable with a given probability density function and using it to find the method of moments estimator. There is some confusion about the given pdf falling under a certain type of distribution, and there is also a discussion about the range of the pdf and the resulting expected value. It is noted that there may be a problem with the expected value being infinite.
  • #1
nontradstuden
54
0

Homework Statement



Find the expected value of a continuous variable y with pdf fy= alpha*y^-2, 0<y<infinity.

I know it is the integral from zero to infinity of y*fy, but I don't know where to go from there. I'm then supposed to use the expected value to find the method of moments estimator.

Does this pdf fall under a certain type of distribution?
Any help is appreciated.
 
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  • #2
What is f(y) if y≤0?

The next step is to write out the integral and carry it out.
But I'm thinking there is information not included?
 
  • #3
@Simon Bridge.

No. That's all I'm given.

f(y;alpha)= alpha*y^(-2) 0<alpha<= y< infinity.

It says to determine E(Y) and then find the method of moments estimator of alpha.The integral of y*fy gives me [alpha* lny] evaluated from zero to infinity, but isn't it infinity?
 
  • #4
f(y;alpha)= alpha*y^(-2) 0<alpha<= y< infinity.
$$f(y;\alpha) = \frac{\alpha}{y^2}: 0 < \alpha \leq y < \infty$$
No. That's all I'm given.
Well then it cannot be done because the function in not determined for part of the range... though the above is already different from what you gave in post #1. Perhaps the extra information is implicit in the context - in your course notes for eg?

Perhaps you are supposed to assume the pdf is zero outside the given range?
The integral of y*fy gives me [alpha* lny] evaluated from zero to infinity, but isn't it infinity?
But maybe you are supposed to evaluate from ##\alpha## to ##\infty##?
Still not much help -

And what about normalization? Isn't $$\int_{-\infty}^\infty f(y)dy=1$$ ... unless ##\alpha## is a function of y?

I'd go ask the person who set the problem. (After checking with other students.)

http://en.wikipedia.org/wiki/Method_of_moments_(statistics )
 
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  • #5
It is standard to define a probability density on a restricted range- it does not have to be from negative infinity to infinity. However, the first given problem, from 0 to infinity has that the problem that the integral goes to infinity. The revised problem, from [itex]\alpha[/itex] to infinity, is good:
[tex]\int_\alpha^\infty \frac{\alpha}{y^2}dy= \alpha\int_\alpha^\infty y^{-2}dy= \alpha\left[-\frac{1}{y}\right]_\alpha^\infty= 1[/tex]

And the expected value is just [itex]\int yf(y)dy[/itex] which, for this problem, is
[tex]\alpha\int_{\alpha}^\infty \frac{1}{y}dy[/tex]
Now that has a problem at infinity!
 
  • #6
nontradstuden said:

Homework Statement



Find the expected value of a continuous variable y with pdf fy= alpha*y^-2, 0<y<infinity.

I know it is the integral from zero to infinity of y*fy, but I don't know where to go from there. I'm then supposed to use the expected value to find the method of moments estimator.

Does this pdf fall under a certain type of distribution?
Any help is appreciated.

There IS NO SUCH probability density function! What you have integrates to +∞ for any α > 0, so cannot integrate to 1. Are you really sure you have written the question correctly?

RGV
 
  • #7
No, it doesn't. The integral of [itex]\alpha y^{-2}[itex], from [itex]\alpha[/itex] to infinity is 1 so it is a pdf. It just does not have finite expected value.
 
  • #8
HallsofIvy said:
No, it doesn't. The integral of [itex]\alpha y^{-2}[itex], from [itex]\alpha[/itex] to infinity is 1 so it is a pdf. It just does not have finite expected value.

OK, I missed the revised statement that had y ε [α,∞) instead of [0,∞).

However, there is still a problem with EY, so I still wonder if the question has been stated correctly.

RGV
 
  • #9
... are the other moments finite?
 

1. What is the formula for finding the expected value of a continuous variable with pdf fy= y^(-2)?

The formula for finding the expected value of a continuous variable with pdf fy= y^(-2) is:

E(X) = ∫y*f(y)dy, where f(y) represents the probability density function (pdf).

2. How do I interpret the expected value in this case?

The expected value is a measure of central tendency that represents the average value of the continuous variable. In this case, it will give you an idea of the typical value of the variable based on its probability distribution.

3. Can I use the same formula to find the expected value for any continuous variable with a different pdf?

Yes, the formula for expected value is general and can be used for any continuous variable with a given pdf. However, the specific form of the integral may vary depending on the pdf.

4. How can I calculate the expected value if I don't have a specific pdf function?

If you do not have a specific pdf function, you can use a numerical approach to estimate the expected value. This involves taking a large number of samples from the variable and calculating the average of these values.

5. Is the expected value the same as the mean of the continuous variable?

Yes, in the context of a continuous variable, the expected value is equivalent to the mean. Both measures represent the average value of the variable, but the expected value is calculated using a probability distribution while the mean is calculated using actual values of the variable.

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