Find the quantity: $P \left(\bigcap_{j=1}^{n} \left \{U_{(j)} > \frac{\alpha j}{n} \right \} \right)$

Question

Let $U_1,…,U_n$ be i.i.d. $U(0,1)$ and $U_{(1)},…,U_{(n)}$ be their order statistics. For $n=1,2,…$, find the quantity: $$ P \left(\bigcap_{j=1}^{n} \left \{U_{(j)} > \frac{\alpha j}{n} \right \} \right) $$
where $0 \le \alpha \le 1$.

My approach:

We can write $$ \begin{align*}
P \left(\bigcap_{j=1}^{n} \left \{U_{(j)} > \frac{\alpha j}{n} \right \} \right) & = E \left[ I_{\bigcap_{j=1}^{n} \left \{U_{(j)} > \frac{\alpha j}{n} \right \}} \right] \\
& = E \left[ \prod_{j=1}^{n} I_{\bigcap_{j=1}^{n} \left \{U_{(j)} > \frac{\alpha j}{n} \right \}} \right] \\
& = E \left[ E \left [ \prod_{j=1}^{n} I_{\bigcap_{j=1}^{n} \left \{U_{(j)} > \frac{\alpha j}{n} \right \}} | U_{(n)} \right ] \right ] \\
& = E \left[ E \left [ \prod_{j=1}^{n} I_{\bigcap_{j=1}^{n} \left \{\frac{U_{(j)}}{U_{(n)}} > \frac{\alpha j}{nt} \right \}} | U_{(n)}=t \right ] \right ] \\
& = E \left[ \prod_{j=1}^{n} P \left \{ \frac{U_{(j)}}{U_{(n)}} > \frac{\alpha j}{nt} \right \} \right ] \ \text{using Basu's Theorem} \\
\end{align*}
$$

I am not sure if I am doing this the right way. I used the fact that $\left \{ \frac{U_{(1)}}{U_{(n)}},…,\frac{U_{(n-1)}}{U_{(n)}} \right \}$ and $U_{(n)}$ are independent. Even if this is correct, how should I compute the probability in the last step? Is there an easier way out?

Thanks.

Answer 1

Probability of the complementary event is $$P\left[\bigcup_{j=1}^n\left\{\frac{nU_{(j)}}{j}\le \alpha\right\}\right]=P\left[\min_{1\le j\le n}\left\{\frac{nU_{(j)}}{j}\right\}\le \alpha\right]$$

Using induction we can show that $T_n=\min\limits_{1\le j\le n}\left\{\frac{nU_{(j)}}{j}\right\}$ has a uniform distribution on $(0,1)$.

For $n=1$, clearly $T_1=U_1 \sim U(0,1)$. Now suppose $T_{k-1}\sim U(0,1)$.

Then for $x\in [0,1]$,

\begin{align} P(T_k \le x)&=\int_0^1 P(T_k\le x \mid U_{(k)}=t)f_{U_{(k)}}(t)\,dt \\&=\int_0^x f_{U_{(k)}}(t)\,dt + \int_x^1 P(T_k\le x \mid U_{(k)}=t)f_{U_{(k)}}(t)\,dt \\&=\int_0^x k t^{k-1}\,dt + \int_x^1 P\left(T_{k-1}\le \left(\frac{k-1}{kt}\right)x \mid U_{(k)}=t \right)kt^{k-1}\,dt \tag{1} \\&= x^k + \int_x^1 \left(\frac{k-1}{kt}\right) x\cdot kt^{k-1}\,dt \tag{2} \\&= x \end{align}

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In $(1)$, we used

\begin{align} T_k \le x &\iff \min_{1\le j\le k-1}\left\{\frac{k U_{(j)}}{j}\right\} \le x \qquad \left[\because\, x<t<1 \right] \\& \iff \min_{1\le j\le k-1}\left\{\frac{(k-1) U_{(j)}}{jt}\right\} \le \left(\frac{k-1}{kt}\right) x \\& \iff T_{k-1} \le \left(\frac{k-1}{kt}\right) x \end{align}

And $(2)$ follows from $(1)$ using the independence of $\left(\frac{U_{(1)}}{U_{(k)}},\frac{U_{(2)}}{U_{(k)}},\ldots,\frac{U_{(k-1)}}{U_{(k)}}\right)$ and $U_{(k)}$.

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So, $T_k \sim U(0,1)$ whenever $T_{k-1}\sim U(0,1)$.

Hence $T_n \sim U(0,1)$ for every $n$ and your desired probability is $1-\alpha$.

Reference:

An improved Bonferroni procedure for multiple tests of significance by R.J. Simes.