Beta Distribution: Find Expected Value of X_(1)

In summary: Thanks again for your help!In summary, the conversation discusses finding the expected value of the minimum of a random sample with a given density. The attempt at a solution involves trying to integrate the density, but encountering difficulties. The conversation then discusses using software such as Maple or Wolfram Alpha to evaluate the integral, and suggests using a binomial expansion method to evaluate it by hand.
  • #1
bonfire09
249
0

Homework Statement



If ##X_1,..,X_n## is a random sample and has a density ##f_X(x)=6x(1-x)## for ##0<x<1##. Let ##X_{(1)}=\min(X_1,..,X_n)##. Find the ##E(X_{(1)})## (Expected Value)?

Homework Equations


There is an uploaded picture of the pdf I used.

The Attempt at a Solution


"I've been stuck on this for hours. If I try integrating it the long way in order to find the expected value I run into problems. " So I tried this way but it seems to be not correct to me. Any help would be great thanks.
So I need to find the ##E(X_{(1)})## (Expected Value) where the pdf of ##X_{(1)}## is
##f_{X_{(1)}}=n*[6x(1-x)*[1-3x^2+2x^3]^{n-1}]## where ##0<x<1##. I know that this looks like some form of a beta distribution.

What I did to find the parameters ##\alpha, \beta## I integrated the pdf of ##X_{(1)}## until I got something that looks like a beta distribution.

We see that $$\int_{0}^{1} 6nx(1-x)*[1-3x^2+2x^3]^{n-1} dx$$ $$=n\int_{0}^{1}6x(1-x)*[1-3x^2+2x^3]^{n-1} dx $$.

Let $u= 3x^2-2x^3$ so it follows that $u'=6x-6x^2$. Plugging this back in we get
$$=n\int_{0}^{1} [1-u]^{n-1} du =n\dfrac{\gamma{(1)}\gamma{(n)}}{\gamma{(n+1)}} \int_{0}^{1} \dfrac{\gamma{(n+1)}}{\gamma{(1)}\gamma{(n)}} [1-u]^{n-1} du.$$ Thus we see that ##X_{(1)}=\mathrm{Beta}(\alpha=1,\beta=n)##. So would this mean from this that ##E(X_{(1)})=\dfrac{1}{n+1}##?.

[/B]
 

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  • #2
bonfire09 said:

Homework Statement



If ##X_1,..,X_n## is a random sample and has a density ##f_X(x)=6x(1-x)## for ##0<x<1##. Let ##X_{(1)}=\min(X_1,..,X_n)##. Find the ##E(X_{(1)})## (Expected Value)?

Homework Equations


There is an uploaded picture of the pdf I used.

The Attempt at a Solution


"I've been stuck on this for hours. If I try integrating it the long way in order to find the expected value I run into problems. " So I tried this way but it seems to be not correct to me. Any help would be great thanks.
So I need to find the ##E(X_{(1)})## (Expected Value) where the pdf of ##X_{(1)}## is
##f_{X_{(1)}}=n*[6x(1-x)*[1-3x^2+2x^3]^{n-1}]## where ##0<x<1##. I know that this looks like some form of a beta distribution.

What I did to find the parameters ##\alpha, \beta## I integrated the pdf of ##X_{(1)}## until I got something that looks like a beta distribution.

We see that $$\int_{0}^{1} 6nx(1-x)*[1-3x^2+2x^3]^{n-1} dx$$ $$=n\int_{0}^{1}6x(1-x)*[1-3x^2+2x^3]^{n-1} dx $$.

Let $u= 3x^2-2x^3$ so it follows that $u'=6x-6x^2$. Plugging this back in we get
$$=n\int_{0}^{1} [1-u]^{n-1} du =n\dfrac{\gamma{(1)}\gamma{(n)}}{\gamma{(n+1)}} \int_{0}^{1} \dfrac{\gamma{(n+1)}}{\gamma{(1)}\gamma{(n)}} [1-u]^{n-1} du.$$ Thus we see that ##X_{(1)}=\mathrm{Beta}(\alpha=1,\beta=n)##. So would this mean from this that ##E(X_{(1)})=\dfrac{1}{n+1}##?.
[/B]

Nope. ##\int_0^1 (1-u)^{n-1} \, du = \frac{1}{n}## (very elementary calculus!), so your integral above = 1. That is as it should be, since you just evaluated ##\int_0^1 f_{X_{(1)}}(x) \, dx##. What you need to evaluate is another integral:
[tex] E X_{(1)} = \int_0^1 x f_{X_{(1)}} (x) \, dx = \int_0^1 6n x^2(1-x) (1 - 3x^2 + 2x^3)^{n-1} \, dx [/tex]
Getting an answer for general ##n## does not look simple, but for specific (small-to-moderate) ##n## it can be done. Here are some examples:
[tex] \begin{array}{cc}
n & E X_{(1)} \\
2 & 13/35\\
3 & 43/140\\
4 & 191/715\\
\vdots & \vdots
\end{array}
[/tex]

While all these integrals are elementary, they are messy enough that I used the computer algebra system Maple to do the work. It can do the integral for n = 100 in seconds, but manual computation would take many hours.

If you do not have access to Maple or Mathematica, you can use the free on-line program Wolfram Alpha (which is a kind of Mathematica lite), but there may be limits on the size of n it can handle.
 
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  • #3
Right I did try to evaluate ##\int_{0}^{1} xf_{x_{(1)}}## but I found it difficult to try to do. I tried to evaluate it on matlab/mathematica and even then it couldn't evaluate it. Not very sure how to go about evaluating the integral.Maybe I'm not seeing it but there could be some way to integrate this. I noticed that there is convolution going on but unfortunately the extra x messes things up.

Wolfram Alpha did give an answer but in a very messy form that I could not really use.
 
Last edited:
  • #4
bonfire09 said:
Right I did try to evaluate ##\int_{0}^{1} xf_{x_{(1)}}## but I found it difficult to try to do. I tried to evaluate it on matlab/mathematica and even then it couldn't evaluate it. Not very sure how to go about evaluating the integral.Maybe I'm not seeing it but there could be some way to integrate this. I noticed that there is convolution going on but unfortunately the extra x messes things up.

You can write
[tex]1-3x^2+2x^3 = 2(1-x)^2 \left(x+\frac{1}{2} \right)[/tex]
to get the density function ##f_n(x)## of ##X_{(1)}## as
[tex] f_n(x) = 6n 2^{n-1} x (1-x)^{2n-1} \left( x + \frac{1}{2} \right)^{n-1}[/tex]
One way to deal with ##\int x f_n(x) \, dx## is to expand ##(x + 1/2)^{n-1}## in a binomial expansion, then integrate term-by-term; each such term will be beta-related. When I do it all in Maple, it manages to arrive at a final expression for ##EX_{(1)}## involving a hypergeometric function.
 
  • #5
I'm assuming that I should be able to do this by hand then? Or will I have to use maple or some other software to this for me through iteration? Well thanks for you help. I'll try this method out tomorrow and see where I get.
 

1. What is the Beta Distribution?

The Beta Distribution is a probability distribution that is often used to model random variables that are constrained to lie between 0 and 1. It is a continuous distribution that is defined by two parameters, alpha and beta.

2. How is the Expected Value of X_(1) calculated for a Beta Distribution?

The Expected Value of X_(1) for a Beta Distribution can be calculated using the formula E[X_(1)] = (alpha / (alpha + beta)). This represents the mean or average value of the minimum of two random variables that are independently and identically distributed with a Beta Distribution.

3. What is the significance of the Expected Value of X_(1) in a Beta Distribution?

The Expected Value of X_(1) in a Beta Distribution is important because it represents the average value of the smallest of two random variables that follow the Beta Distribution. This can be useful in predicting the minimum value that a given variable can take on.

4. How does changing the values of alpha and beta affect the Expected Value of X_(1) in a Beta Distribution?

The values of alpha and beta in a Beta Distribution can affect the Expected Value of X_(1) in different ways. Generally, increasing the value of alpha will increase the Expected Value of X_(1), while increasing the value of beta will decrease it. However, the exact impact will depend on the specific values of alpha and beta.

5. Can the Expected Value of X_(1) be negative in a Beta Distribution?

No, the Expected Value of X_(1) cannot be negative in a Beta Distribution. This is because the Beta Distribution is defined to have a range of 0 to 1, so the minimum value it can take on is 0. Therefore, the Expected Value of X_(1) will always be greater than or equal to 0.

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