Proofing Varience in Gamma Distribution

In summary, the problem involves finding E(1/y^2) and proving Var(1/y) using the given equation for Fy(y) and assumption for E(1/y). The solution involves setting up an integral and solving for the expected value, then using that to calculate the variance. The original attempt at the solution involved squaring y instead of 1/y, but the mistake was corrected to get the correct solution.
  • #1
Philip Wong
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Homework Statement


Fy(y) = 1/2 λ^3 y^2 e^(-λy)

Assume E(1/y) = λ/2
Find E(1/y^2) and prove Var(1/y) = λ^2 / 4


Homework Equations


E(y) = k/λ
Var (y) = k/ λ^2

The Attempt at a Solution


Hi guys,

From the equation given from gamma distribution, I understands clearly that to get Var(y) what I have to do is to square y. i.e. 1/y^2 = (λ/2)^2 = λ^2 / 4. Which is equivalent to E(1/y^2)

but I'm no clue on how to set up a proof to show this is correct. I needed help on this, as I have no idea at all on how to proof this.
 
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  • #2
the problem doesn't ask fior var(y) it asks for var(1/y)

the expectation of any function of y, say g(y), can be written
[tex] E(g(y)) = \int g(y) f_Y(y)dy[/tex]

so for E(1/y^2) this gives
[tex] E(\frac{1}{y^2}) = \int \frac{1}{y^2} f_Y(y)dy[/tex]
 
  • #3
Ok I've retried to do E(X). Can you please check if I have done it correctly before I moved on trying to work out the variance.

E(1/y^2) = ∫1/y^2 fy(y) dy
= ∫ 1/4 * λ^2 * 1/2 * λ^3 * y^2 * e^-λy dy
=∫1/8 * λ^5 * y^2 * e^-λy dy
=1/8 * 1/3 * λ^5 * y^3 * e^-λy - 1/λ * λ^6 * y^2 * e^-λy
=1/8 * 1/3 * λ^5 * y^3 * e^-λy - λe^-λy * λ^5 * y^2
=1/24 * λ^5 * y^3 * e^-λy - λ^6 * y^2 * e^-λy
=1/24 * λ^5 * y^2 * e^-λy (y-λ)

Up to this point I knew I did something wrong. But I run over my calculation again, but doesn't seem to be able to pick out where I did wrong.

It would be nice if you can pick out where I did wrong. thanks
 
  • #4
what happened to the 1/y^2 in the 2nd line?
 
  • #5
ar! I see where I get wrong now. I misunderstood it as (EX)^2 and substitute in the its value as 1/y^2. I got it now. should be

∫1/y^2 * fx(x) dy
∫1/y^2 * 1/2 λ^3 * y^2 * e(-λy) dy
∫1/2 * λ^3 * e(-λy) dy

= 1/2 λ^3 - 1/λ e(-λy)
 
  • #6
bit hard to read or follow, but if you mean times (-1/gamma) you're on the right track
 

FAQ: Proofing Varience in Gamma Distribution

What is a gamma distribution?

A gamma distribution is a type of probability distribution that is commonly used to model variables that have skewed and positive values. It is characterized by two parameters: shape and scale.

Why is proofing variance important in gamma distribution?

Proofing variance is important in gamma distribution because it allows us to assess the variability of the data and make inferences about the population. It also helps in determining the accuracy and precision of the estimated parameters.

How is proofing variance done in gamma distribution?

Proofing variance in gamma distribution is typically done by calculating the standard deviation or variance of the data. This can be done using statistical software or by hand using the formula for variance or standard deviation.

What are the assumptions for proofing variance in gamma distribution?

There are several assumptions that need to be met in order to proof variance in gamma distribution. These include the assumption of independence between observations, the assumption of a linear relationship between the mean and the variance, and the assumption of normally distributed errors.

What are common methods for proofing variance in gamma distribution?

Some common methods for proofing variance in gamma distribution include graphical methods such as scatterplots and histograms, as well as statistical tests such as the F-test or chi-square test. Other methods include calculating confidence intervals or using Bayesian inference techniques.

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