# probability density of the maximum of samples from a normalized uniform distribution

Suppose

$$X_1, X_2, \dots, X_n\sim Unif(0, 1), iid$$

and suppose

$$\hat\theta = \max\{X_1, X_2, \dots, X_n\} / \sum_i^nX_i$$

How would I find the probability density of $\hat\theta$?

I know the answer if it's iid. But I don't know how to formalize the fact that the sum is iqual to 1.

a simiar question can be found here: probability density of the maximum of samples from a uniform distribution

I arrive here: \begin{align} P(Y\leq x)&=P(\max(X_1,X_2 ,\cdots,X_n)/\sum_i^nX_i\leq x)\\&=P(X_1/\sum_i^nX_i\leq x,X_2/\sum_i^nX_i\leq x,\cdots,X_n/\sum_i^nX_i\leq x)\\ &\stackrel{ind}{=} \prod_{j=1}^nP(X_j/\sum_i^nX_i\leq x )\\& \ \ \ \ \ \end{align}

• Something funny about that link. Commented Jan 11, 2016 at 13:35
• I added an edit to the queue with a fixed link. Commented Jan 11, 2016 at 13:37
• Sorry but "Suppose $X_1, X_2, \dots, X_n\sim Unif(0, \theta)$" and "such that $X_1 + X_2 + \dots + X_n = 1$" are not compatible (except if $n\theta=2$). Please explain.
– Did
Commented Jan 11, 2016 at 13:38
• thanks did, I will reformulate my problem Commented Jan 11, 2016 at 13:48
• One interesting thing here is that $\theta$ is a scaled parameter - you can write $X_i = \theta Y_i$ where $Y_i \sim \text{Uniform}(0, 1)$. So the "estimator" $\hat{\theta}$ itself, is independent of the parameter of interest $\theta$.
– BGM
Commented Jan 15, 2016 at 4:51

Note that $$\hat\theta_n\sim \frac 1 {1+\sum_{i=1}^{n-1}U_i}$$ for i.i.d. standard uniforms $U_i$. Now see this and this

• This looks correct, but it does not look obvious that $X_i/X_M$ and $X_j/X_M$ are independent... could you elaborate? Commented Jan 20, 2016 at 19:10
• @leon They are conditionally independent given $X_M$ and have distributions independent of $X_M$.
– A.S.
Commented Jan 20, 2016 at 19:52
• I can but that, I'm saying that it's not obvious. Commented Jan 20, 2016 at 21:56
• @leon I'm not sure what to add. It's rather clear (as in intuitive without justification) to me and can be easily supported: $$P(A_i<t_i,A_j<t_j|X_m)=P(X_i<t_i X_m,X_j<t_j X_m|X_m)=P(X_i<t_i X_m|X_m)P(X_j<t_j X_m|X_m)=P(A_i<t_i|X_m)P(A_j<t_j|X_m)=P(A_i<t_i)P(A_j<t_j)$$ and take expectation of both sides.
– A.S.
Commented Jan 20, 2016 at 22:18

If this is any help, here are some simulations of the density.

Because of symmetry, it is sufficient to only look at the cases where $X_1$ is the maximum. In that case, $X_2, \dots, X_n$ are independent and uniformly distributed between 0 and $X_1$. $$\theta = \frac{X_1}{X_1 + \sum_{i=2}^n X_i} \quad \text{with} \ X_2, \dots, X_n \sim U(0, X_1)$$ Now we divide by $X_1$ on both sides of the fraction and we get the formula A.S. gave us. $$\theta = \frac{1}{1 + \sum_{i=2}^n X_i} \quad \text{with} \ X_2, \dots, X_n \sim U(0, 1)$$ The sum of $n$ iid standard uniform random variables has the Irwin–Hall distribution. It's PDF (probability density function) is: $$f(x) = \frac{1}{2\left(n-1\right)!}\sum_{k=0}^n\left(-1\right)^k{n \choose k}\left(x-k\right)^{n-1}\operatorname{sgn}(x-k)$$ Let $$X = \sum_{i=2}^n X_i$$ The PDF of $X$ is: $$f_X(x) = \frac{1}{2\left(n-2\right)!}\sum_{k=0}^{n-1}\left(-1\right)^k{n-1 \choose k}\left(x-k\right)^{n-2}\operatorname{sgn}(x-k)$$ Now we can use change of variable to calculate the PDF of $\theta$. The following formula gives the PDF of $\theta$ if $\theta = g(X)$ and $g(x)$ is monotonic.

$$f_\theta(y) = \left| \frac{\mathrm{d}}{\mathrm{d}y} (g^{-1}(y)) \right| \cdot f_X(g^{-1}(y))$$ We have $$\begin{array}{rl} g(x) &= \frac{1}{1 + x} \\ g^{-1}(y) &= 1/y - 1 \\ \left| \frac{\mathrm{d}}{\mathrm{d}y} g^{-1}(y) \right| &= y^{-2} \end{array}$$ So the PDF of $\theta$ is: $$\begin{array}{rl} f_\theta(y) &= \displaystyle \frac{1}{2 y^2 \left(n-2\right)!}\sum_{k=0}^{n-1}\left(-1\right)^k{n-1 \choose k}\left(1/y-1-k\right)^{n-2}\operatorname{sgn}(1/y-1-k) \\ &= \displaystyle \frac{-1}{2 y^2 \left(n-2\right)!}\sum_{k=1}^{n}\left(-1\right)^k{n-1 \choose k-1}\left(1/y-k\right)^{n-2}\operatorname{sgn}(1/y-k) \end{array}$$ It is positive at $y \in (1/n, 1)$.

## Approximation for large $n$

The mean and the variance of the Irwin-Hall distribution are respectively $\mu=n/2$ and $\sigma^2=n/12$. Because the Irwin-Hall distribution is the sum of $n$ iid random variables, the central limit theorem states that for large $n$ its distribution is very close to the normal distribution with the same mean and variance. The normal distribution has PDF: $$f(x) = \frac{1}{\sigma\sqrt{2\pi} } \; \exp\left( -\frac{(x-\mu)^2}{2\sigma^2} \right)$$ Replacing $\mu$ and $\sigma^2$ with the mean and variance of the Irwin-Hall distribution with parameter $n-1$ gets us: $$f_X(x) \approx \frac{1}{\sqrt{\pi (n-1)/6} } \; \exp\left( -\frac{(x-(n-1)/2)^2}{(n-1)/6} \right)$$ Using the same change of variable technique as above, we get the distribution of $\theta$ for large $n$: $$\begin{array}{rl} f_\theta(y) &\approx \displaystyle \frac{1}{y^2\sqrt{\pi (n-1)/6} } \; \exp\left( -\frac{(1/y-1-(n-1)/2)^2}{(n-1)/6} \right) \\ &= \displaystyle \frac{1}{y^2\sqrt{\pi (n-1)/6} } \; \exp\left( -\frac{3}{2}\cdot\frac{(2/y-n-1)^2}{n-1} \right) \end{array}$$

Let me first rephrase the problem a little bit

$$P(\theta)d\theta = \left[\int_{0}^1 dy P(\max\{X_i\}=y)\cdot P\left(\bar{X}=\frac{y}{n\theta} \middle| \max \{X_i\}=y \right)\right]d\bar{X}$$

It is mentioned in this link that $$P(\max\{X_i\}=y)=y^n$$

Without losing generosity, I can also reorder the ${X_i}$ to be ${\mu_i}$,so that $\mu_n=\max\{X_i\}=y$.

Now I can define $\bar{\mu}=\sum_{i=0}^{n-1}\mu_i/(n-1)$ , thus $$P\left(\bar{X}=\frac{y}{n\theta} \middle| \max \{X_i\}=y \right)d\bar{X} = P\left(\bar{\mu}=\frac{1-\theta}{(n-1)\theta}y \middle| \mu_i \sim Unif(0,y)\right)d\bar{\mu}$$

Further define $z_i=y\mu_i$, we can write

$$P(\theta)d\theta = \int_{0}^1 dy d\bar{\mu} \left[ y^n\cdot P\left(\bar{\mu}=\frac{1-\theta}{(n-1)\theta}y \middle| \mu_i \sim Unif(0,y) \right)\right] \\ = \int_{0}^1 dy \underbrace{ d\bar{z} \left[P\left(\bar{z}=\frac{1-\theta}{(n-1)\theta}y \middle| z_i \sim Unif(0,1) \right)\right] }_{P_0}$$

Notice the part I labeled as $P_0$ is independent of $y$, so we can carry out the integral trivially. $$P(\theta)d\theta = P_0= \left[P\left(\bar{z}=\frac{1-\theta}{(n-1)\theta}y \middle| z_i \sim Unif(0,1) \right)\right] d\bar{z} \\ =\left[P\left(\bar{z}=\frac{1-\theta}{(n-1)\theta}y \middle| z_i \sim Unif(0,1) \right)\right] \left|\frac{1}{(n-1)\theta^2}\right|d\theta$$

I believe there is some general analytic form for this distribution of $\bar{z}$ for arbitrary $n$, but I just can't solve that. However, there are some solvable examples to test this formula:

n=2: $$P(\theta)=\left[P\left(z_1=\frac{1-\theta}{\theta} \middle| z_1 \sim Unif(0,1) \right)\right] \frac{1}{\theta^2} = \frac{1}{\theta^2}$$

n=3: $$P(\theta)=\left[P\left(\frac{z_1+z_2}{2}=\frac{1-\theta}{2\theta} \middle| z_1,z_2 \sim Unif(0,1) \right)\right] \frac{1}{2\theta^2} =\left[2-4*\left|\frac{1-\theta}{2\theta}-0.5\right|\right] \frac{1}{2\theta^2}$$

n is large

When $n$ is large, from central limit theorem, we know that $$P\left(\bar{z}=\frac{1-\theta}{(n-1)\theta} \middle| z_i \sim Unif(0,1) \right) \approx P_\text{Gauss}\left(\frac{1-\theta}{(n-1)\theta}, \mu=0.5, \sigma^2=\frac{1}{12n}\right)\\ =\sqrt{\frac{6n}{\pi}}\exp\left[-6n\left(\frac{1-\theta}{(n-1)\theta}-0.5\right)^2\right]$$

With some approximation $n\gg 1$, we can write down the form more neatly as $$\lim_{n\to \infty}P(\theta)\approx \frac{1}{n\theta^2} \sqrt{\frac{6n}{\pi}}\exp\left[-6n\left(\frac{1-\theta}{n\theta}-0.5\right)^2\right]$$

MLE with large n The value of maximum probability density is approximately $$\frac{1-\theta}{n\theta}=0.5 \Rightarrow \theta=\frac{2}{n}$$

• Sorry but this falls into a serious trap already in the very first lines: for every $y$, $P(\max\{X_i\}=y)=0$, not $y^n$. Still in the first displayed formula, $d\bar X$ is another problem.
– Did
Commented Jan 20, 2016 at 17:59
• @Did why the probability density of $P(\max\{X_i\})=y$ is $0$? can you elaborate more? Commented Jan 20, 2016 at 19:21
• @Did I agree the $P(\bar{X}=f(\theta))d\bar{X}$ notation is little sloppy, the more rigorous way of writing it should be $\int P(\bar{X})\delta[\bar{X}-f(\theta)]d\bar{X}$. Is this the problem you are complaining about? Commented Jan 20, 2016 at 19:24
• This is simply because each random variable $X_i$ is continuous hence $P(X_i=y)=0$ for every $i$ and $y$ hence $P(\max\limits_i\{X_i\}=y)\leqslant\sum\limits_iP(X_i=y)=0$. And, by the way, the link you mention does not say that $P(\max\limits_i\{X_i\}=y)=y^n$ but that $P(\max\limits_i\{X_i\}\leqslant y)=y^n$.
– Did
Commented Jan 21, 2016 at 1:20
• Second problem: if $\bar X$ is a random variable, I fail to understand what $d\bar X$ refers to.
– Did
Commented Jan 21, 2016 at 1:21