4
$\begingroup$

Let $ Y_1 < Y_2 <\ldots <Y_{10}$ be the order statistics of a random sample from a continuous type distribution with cdf $F(x)$. How would I begin to show that the joint distribution of $V_1=F(Y_4)-F(Y_2)$ and $V_2=F(Y_{10})-F(Y_6)$ is given by:

$$h(v_1,v_2)= \frac{10!}{1!3!4!} v_1 v_2^3 (1-v_1-v_2)^4 $$

Any hints? Thank you.

$\endgroup$
5
  • 1
    $\begingroup$ @wolfies This is all the information I am given. $\endgroup$
    – JohnK
    Commented Dec 4, 2013 at 16:20
  • $\begingroup$ @wolfies This is precisely what I am suggesting. This is a general condition that holds and people who have seen order statistics, tolerance intervals and coverages before might be able to help me derive it. $\endgroup$
    – JohnK
    Commented Dec 4, 2013 at 16:28
  • $\begingroup$ And that's all the information you need: $F$ is the probability integral transform so indeed $F(Y_i)$ is distributed as the $i^\text{th}$ order statistic from a uniform distribution. For a non-rigorous (but nevertheless accurate) heuristic, think of this order statistic as dividing the interval $[0,1]$ into three bins of length $F(Y_i)$, $dY_i$ (an infinitesimal), and $1-F(Y_i)$, into which precisely $i-1$, $1$, and $n-i$ of the values fall (that's where the multinomial coefficients come from). $\endgroup$
    – whuber
    Commented Dec 4, 2013 at 16:31
  • $\begingroup$ @whuber It's the functions that I having trouble comprehending. And by functions I mean the difference between the cdf values$v_1,v_2$. Why is the unit interval split like that precisely? $\endgroup$
    – JohnK
    Commented Dec 4, 2013 at 16:34
  • $\begingroup$ Second time this has come up - lol - and I learn not from my errors :) $\endgroup$
    – wolfies
    Commented Dec 4, 2013 at 16:36

1 Answer 1

7
$\begingroup$

This is a nonrigorous demonstration (at least as far as those who don't use nonstandard analysis are concerned) that provides some intuition for working with order statistics.

The function $F$ makes the distribution uniform. Consider four positions $0\lt x_2\lt x_4\lt x_6\lt x_{10}\lt 1$ and four corresponding "infinitesimal" lengths $dx_2, \ldots, dx_{10}$. These determine eight locations within the unit interval which thereby divide it into nine bins corresponding to the colored inequality symbols:

$$0\color{red}\le x_2\color{red}\lt x_2+dx_2\color{red}\le x_4\color{red}\lt x_4+dx_4\color{red}\le x_6\color{red}\lt x_6+dx_6\color{red}\le x_{10}\color{red}\lt x_{10}+dx_{10}\color{red}\le 1.$$

By ignoring events of zero probability, we readily deduce that the first bin, $[0, x_2),$ is occupied by precisely one value, $x_1,$ because $0\le x_1\lt x_2$ (by definition of order statistics). The use of "$\lt$" instead of "$\le$" ignores the zero chance that $x_1=x_2$; this is where continuity is needed.

Similarly, the bin $[x_2+dx_2, x_4)$ is occupied only by $x_3$; the bin $[x_4+dx_4, x_6]$ is occupied only by $x_5$, and $[x_6+dx_6, x_{10})$ is occupied by $x_7, x_8,$ and $x_9$. The four infinitesimal bins $[x_2, x_2+dx_2)$, $[x_4, x_4+dx_4)$, $[x_6, x_6+dx_6),$ and $[x_{10}, x_{10} + dx_{10}),$ are occupied by $x_2, x_4, x_6,$ and $x_{10},$ respectively. Finally, the bin $[x_{10}+dx_{10}, 1]$ has no occupants.

The chance that a random uniform value occupies any bin is equal to the bin's length. The chance of any particular collection of occupancy numbers is given by a multinomial distribution; in this case it equals (up to the lowest order of infinitesimals)

$$\binom{10}{1,1,1,1,1,1,3,1,0}x_2^1(x_4-x_2)^1(x_6-x_4)^1(x_{10}-x_6)^3(1-x_{10})^0dx_2dx_4dx_6dx_{10}.$$

(By definition, the multinomial coefficient $\binom{10}{1,1,1,1,1,1,3,1,0}=\frac{10!}{1!1!1!1!1!1!3!1!0!}=\frac{10!}{3!}.$)

Changing variables to $x_4 = x_2+v_1$ and $x_6 = x_{10}-v_2$ in order to represent the ranges $v_1=x_4-x_2$ and $v_2=x_{10}-x_6$ turns this into

$$\left(\frac{10!}{3!}v_1v_2^3dv_1dv_2\right)\left(x_2(x_{10}-x_2-v_1-v_2)) dx_2dx_{10}\right).$$

Performing the integrations for $x_2$ and $x_{10}$, subject to the order restrictions $0\le x_2\le 1-v_1-v_2$ and $x_2+v_1+v_2\le x_{10}\le 1$, produces the joint PDF of $(v_1,v_2)$ given in the question. Its natural support is where $0\le v_1\le 1,$ $0\le v_2\le 1$, and $v_1+v_2\le 1.$


A computer algebra system can make the calculations less painful. Here is a Mathematica 9 solution:

Integrate[
 PDF[OrderDistribution[{UniformDistribution[], 10}, {2, 4, 6, 10}]][{x2, x4, x6, x10}] 
    /. {x4 -> x2 + v1, x6 -> x10 - v2}, {x2, 0, 1}, {x10, x2, 1}]

$\begin{array}{cc} \{ & \begin{array}{cc} 25200 \text{v1} \text{v2}^3 (\text{v1}+\text{v2}-1)^4 & 0<\text{v1}<1\land \text{v2}\geq 0\land \text{v1}+\text{v2}<1 \\ 0 & \text{True} \\ \end{array} \\ \end{array}$

$\endgroup$
3
  • $\begingroup$ Reviewing your answer, the only trouble I am having is justifying the order restrictions for $x_2$ and $x_{10}$. Would you mind giving me the intuition there? $\endgroup$
    – JohnK
    Commented Dec 4, 2013 at 22:16
  • $\begingroup$ The restrictions are immediate from the definitions of order statistics and the $v_i$: we know that two of the bins between $x_2$ and $x_{10}$ have widths of $v_1$ and $v_2$ (while the rest can be arbitrarily small), so $x_2+v_1+v_2$ cannot exceed $x_{10}$. $\endgroup$
    – whuber
    Commented Dec 4, 2013 at 22:31
  • 1
    $\begingroup$ Statistics is fascinating, isn't it? $\endgroup$
    – JohnK
    Commented Dec 4, 2013 at 22:36

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.