Characterising argmax of uniform distributions I was thinking about comparisons of uniform random variables of the type $U(0,T)$, when I began to wonder about the argmax. Consider a sequence of parameters $T_1\le T_2\le\ldots T_n$ and corresponding independent random variables $U_1 \sim U(0,T_1)$, $U_2 \sim U(0,T_2)$ and so on. Now define a random variable $M=\arg\max_{0\le i\le n} U_i$. Is it true that $P(M=k)\uparrow k\uparrow$?


*

*I started with $P(M=k)=P(U_k\ge U_1, U_k\ge U_2, \ldots U_k\ge U_{k-1},U_k\ge U_{k+1},\ldots U_k\ge U_n)$. 

*Here I use: $P(U_i\ge U_j)=\frac{T_i}{2T_j}$ if $T_i\le T_j$ and $P(U_i\ge U_j)=1-\frac{T_j}{2T_i}$ if $T_i\ge T_j$.

*Simplifying, 


$$P(M=k)=\prod_{i=1}^{k-1}\left(1-\frac{T_i}{2T_k}\right)\times \prod_{j=k+1}^n\left(\frac{T_k}{2T_j}\right)$$
It is here that I am stuck. I tried to use $\frac{P(M=k)}{P(M=k+1)}$, but the expression seems to be unwieldy. Is there a way to see my conjecture to conclusion?  
 A: Suppose $X=\max \{ U_1,\ldots,U_n\}$ and suppose $T_k \lt T_{k+1}$ [equality is easy to deal with].
Then  


*

*if $x \lt T_k \lt T_{k+1}$: $\qquad 0 \lt \Pr(M=k|X=x) = \Pr(M=k+1|X=x)$

*if $T_k \lt x \lt T_{k+1}$: $\qquad 0 = \Pr(M=k|X=x)  \lt \Pr(M=k+1|X=x)$   

*if $T_k \lt T_{k+1} \lt x$: $\qquad 0 = \Pr(M=k|X=x)  = \Pr(M=k+1|X=x)$   


and by integrating over $x$, you get  $\Pr(M=k) \lt \Pr(M=k+1)$, so $\Pr(M=k)$ is an increasing function of $k$.
A: Your formula for $\mathrm P(M=k)$ is wrong because the random variables $U_i$ for $i\ne k$ are not independent.
In fact, $[M=k]=[U_k\gt V_k]$ with $V_k=\max\limits_{i\ne k}U_i$ hence $\mathrm P(M=k)=\mathrm P(V_k\lt U_k)$. Since the random variables $U_k$ and $V_k$ are independent, integrating this identity with respect to the distribution of $U_k$ yields
$$
\mathrm P(M=k)=\frac1{T_k}\int_0^{T_k}\mathrm P(V_k\lt u)\mathrm du=\frac1{T_k}\sum\limits_{i=1}^k\int_{T_{i-1}}^{T_i}\mathrm P(V_k\lt u)\mathrm du,
$$
with the notation $T_0=0$. If $T_{i-1}\lt u\lt T_i$ with $i\leqslant k$, then $U_j\lt u$ with full probability for every $j\leqslant i-1$ hence $[V_k\lt u]=[\forall j\geqslant i,j\ne k,U_j\lt u]$ and
$$
\mathrm P(M=k)=\frac1{T_k}\sum\limits_{i=1}^k\int_{T_{i-1}}^{T_i}\prod\limits_{i\leqslant j\leqslant n,j\ne i}\frac{t}{T_j}\mathrm dt,
$$
that is,
$$
\mathrm P(M=k)=\sum\limits_{i=1}^k\frac{T_i^{n-i+1}-T_{i-1}^{n-i+1}}{(n-i+1)T_iT_{i+1}\cdots T_n}=\sum\limits_{i=1}^k\frac{q_i-q_{i-1}}{n-i+1},
$$
where, for every $0\leqslant i\leqslant n$,
$$
q_i=\frac{T_i^{n-i}}{T_{i+1}T_{i+2}\cdots T_n}.
$$
One sees that $\mathrm P(M=k)\leqslant\mathrm P(M=k+1)$ and that the inequality is strict as soon as $T_i\lt T_{i+1}$ since
$$
q_{i+1}=q_i\left(\frac{T_{i+1}}{T_i}\right)^{n-i}.
$$

A simple construction of $M$ which might help to explain why the result is true is as follows. Consider a random index $1\leqslant I\leqslant n$ whose distribution is characterized by the fact that, for every $1\leqslant i\leqslant n$,
$$
\mathrm P(I\leqslant i)=q_i,\qquad \mathrm P(I=i)=q_i-q_{i-1}.
$$
Then, conditionally on $I$, $M$ is uniformly distributed in the set $\{I,I+1,\ldots,n\}$.

This yields directly the explanation you are after. Choose $1\leqslant k\leqslant n$. Then:


*

*If $i\leqslant k$, then $\mathrm P(M=k\mid I=i)=\mathrm P(M=k+1\mid I=i)$.

*For $i=k+1$, $\mathrm P(M=k\mid I=k+1)=0$ while $\mathrm P(M=k+1\mid I=k+1)\gt0$.

*If $i\gt k+1$, then $\mathrm P(M=k\mid I=i)=0=\mathrm P(M=k+1\mid I=i)$. 


Summing these contributions yields $\mathrm P(M=k)\lt\mathrm P(M=k+1)$. This also yields once again the difference
$$
\mathrm P(M=k+1)-\mathrm P(M=k)=\mathrm P(M=I=k+1)=\frac{\mathrm P(I=k+1)}{n-k}=\frac{T_{k+1}^{n-k}-T_k^{n-k}}{(n-k)T_{k+1}\cdots T_n}.
$$
