Beta/F Distribution: Show Y has Beta Dist.

In summary, the conversation discusses the distribution of Y, which is defined as 1/(1+(r1/r2)W), where W has a beta distribution. It is shown that Y also has a beta distribution by transforming the expression to the form (r1/r2)W/(1+(r1/r2)W).
  • #1
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Homework Statement



Let Y = [tex]\frac{1}{1 + \frac{r_1}{r_2}W}[/tex] and W ~ F(r1,r2). Show that Y has a beta distributoin

Homework Equations


The Attempt at a Solution



I know that
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and
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, then Y has a beta distribution.

Not sure what to do next.
 
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  • #2
If W is F(m,n), then 1/W is F(n,m).
 
  • #3
Sorry, but I'm still having problems with the tranformation.
 
  • #4
Just multiply the numerator and denominator of [tex]\frac{1}{1 + \frac{r_1}{r_2}W}[/tex] by an appropriate quantity, to put it in the form [tex]\frac{\frac{\nu_1}{\nu_2}X}{\frac{\nu_1}{\nu_2}X + 1}[/tex]
 
  • #5
So:

[tex]\frac{1}{1 + \frac{r_1}{r_2}W}\frac{\frac{r_2}{r_1}\frac{1}{W}}{\frac{r_2}{r_1}\frac{1}{W}} = \frac{\frac{r_2}{r_1}\frac{1}{W}}{\frac{r_2}{r_1}\frac{1}{W} + 1} = \frac{\frac{r_1}{r_2}W}{\frac{r_1}{r_2}W + 1} [/tex]
 
  • #6
So:

[tex]\frac{1}{1 + \frac{r_1}{r_2}W}\frac{\frac{r_2}{r_1}\frac{1}{W}}{\frac{r_2}{r_1}\frac{1}{W}} = \frac{\frac{r_2}{r_1}\frac{1}{W}}{\frac{r_2}{r_1}\frac{1}{W} + 1}[/tex]

Yes. Since 1/W is F(r2,r1), you are done.

[tex] = \frac{\frac{r_1}{r_2}W}{\frac{r_1}{r_2}W + 1} [/tex]

No.
 
  • #7
so I can just leave it as [tex]\frac{\frac{r_2}{r_1}\frac{1}{W}}{\frac{r_2}{r_1} \frac{1}{W} + 1}[/tex] ?
 
  • #8
I would substitute X for 1/W.
 
  • #9
Thanks for the help.
 

FAQ: Beta/F Distribution: Show Y has Beta Dist.

What is the Beta distribution?

The Beta distribution is a continuous probability distribution that is commonly used in statistics. It is defined by two parameters, alpha and beta, which determine the shape of the distribution. The Beta distribution is often used to model continuous data that is bounded between 0 and 1.

How is the Beta distribution related to the F distribution?

The Beta distribution is a special case of the F distribution, where the numerator degrees of freedom is equal to 1. This means that if a random variable Y has a Beta distribution, then Y can also be expressed as the ratio of two independent chi-squared variables divided by their respective degrees of freedom.

What is the relationship between the Beta distribution and the binomial distribution?

The Beta distribution can be used as a conjugate prior for the binomial distribution. This means that if the prior distribution for a binomial parameter is a Beta distribution, then the posterior distribution will also be a Beta distribution. This makes the Beta distribution a useful tool in Bayesian inference for binomial data.

What is the expected value of a random variable with a Beta distribution?

The expected value, or mean, of a random variable Y with a Beta distribution is equal to the first parameter, alpha, divided by the sum of both parameters, alpha and beta. In mathematical notation, this can be written as E[Y] = alpha / (alpha + beta).

Can the Beta distribution be used for non-negative continuous data that is not bounded between 0 and 1?

No, the Beta distribution is only suitable for data that is bounded between 0 and 1. If the data is not bounded, then another distribution, such as the Uniform distribution, may be more appropriate for modeling the data.

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