Order statistics and a convolution

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SUMMARY

The discussion focuses on solving a problem related to order statistics and convolution involving random samples from a uniform distribution on the interval [0,1]. The probability density functions (pdf) for the smallest and largest order statistics, Y_{1}' and Y_{n}', are derived as f_{Y_{1}'} = n(1-y)^{n-1} and f_{Y_{n}'} = ny^{n-1}, respectively. The expected value E(Y_{n}' - Y_{1}') is calculated as (n-1)/(n+1), and the pdf for Y_{2} is confirmed as f_{Y_{2}} = n(n-1)y(1-y)^{n-2}. The convolution integral for the sum of two random variables is also discussed, with the expected result being f_{X}(x) = 1 - |1-x| for 0 ≤ x ≤ 2.

PREREQUISITES
  • Understanding of order statistics in probability theory
  • Familiarity with probability density functions (pdf) and cumulative distribution functions (cdf)
  • Knowledge of convolution in probability distributions
  • Basic calculus for evaluating integrals
NEXT STEPS
  • Study the derivation of order statistics pdfs in depth
  • Learn about the properties and applications of convolution in probability
  • Explore the concept of expected values in the context of random variables
  • Practice solving integrals involving probability density functions
USEFUL FOR

Students studying probability theory, statisticians working with uniform distributions, and anyone interested in the applications of order statistics and convolution in statistical analysis.

strangequark
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Homework Statement



Hi, I'm having some problem with one of my final exam study questions, and I'm hoping someone can help me out a little.

here is the problem:

Let [tex]Y_{1},Y_{2},...,Y_{n}[/tex] denote random samples of numbers from a uniform distribution on the interval [0,1]. Denote the largest and smallest numbers as [tex]Y_{n}'[/tex] and [tex]Y_{1}'[/tex].

a. Find the pdf and cdf of [tex]Y_{1}'[/tex] and[tex]Y_{n}'[/tex].
b. Find [tex]E(Y_{n}'-Y_{1}')[/tex]
c. Show that [tex]\lim_{n\to\infty}E(Y_{n}'-Y_{1}')=1[/tex]
d. Find the pdf for [tex]Y_{2}[/tex] and find the pdf for [tex]Y=Y_{1}+Y_{2}[/tex]

Homework Equations



the ith order statistic:

[tex]f_{Y_i}=\frac{n!}{(i-1)!(n-i)!}(F_{Y}(y))^{i-1}(1-F_{Y})^{n-i}f_{Y}(y)[/tex]

convolution:
if [tex]X=Y_{1}+Y_{2}[/tex]

[tex]f_{X}(x)=\int^{x}_{0}f_{Y_{1}}(y)f_{Y_{2}}(x-y)dy[/tex]

The Attempt at a Solution



i have:
a.

ok, so I am pretty sure that my pdf's will be:
[tex]f_{Y_{1}'}=\frac{n!}{(n-1)!}(1-y)^{n-1}=n(1-y)^{n-1}[/tex]
and
[tex]f_{Y_{n}'}=\frac{n!}{(n-1)!}(y)^{n-1}=ny^{n-1}[/tex]

b. cumulative functions easy from above.

c.
I get
[tex]E(Y_{n}')=\frac{n}{n+1}[/tex]
and
[tex]E(Y_{1}')=\frac{1}{n+1}[/tex]
so:
[tex]E(Y_{n}'-Y_{1}')=\frac{n-1}{n+1}[/tex]

d. here's where I'm confused... for [tex]f_{Y_{1}}[/tex] and [tex]f_{Y_{2}}[/tex], I have:

[tex]f_{Y_{1}}=n(1-y)^{n-1}[/tex]

Is my [tex]f_{Y_{2}}[/tex] correct?

[tex]f_{Y_{2}}=n(n-1)y(1-y)^{n-2}[/tex]

if it is, how the heck do I calculate this integral??

[tex]\int^{x}_{0}n(n)(n-1)(1-y)^{n-1}(x-y)(1-(x-y))^{n-2}dy[/tex]

I'm not seeing my mistake. The answer should be:

[tex]f_{X}(x)=1-|1-x|[/tex] for [tex]0\leqx\leq2[/tex]

help?
 
Last edited:
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[itex]f_{Y_{2}}(z)[/itex] is n!/((n-k)!(k-1)!)F(z)k-1[(1-F(z)]n-kf(z) = n(n-1)y(1-y)n-2, so it is correct.
 

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