Here's a problem that seems rather peculiar to me. This time I have no initial idea about how to solve it.
Let $X_1$, ..., $X_n$ be independent, real valued random variables with density $f$ and CDF $F$. Let $F_i$ denote the CDF of $X_{(i)}$.
a) What is the distribution of $F(X_{(i)})$ and $F_i(X_{(i)})$?
b) What is the variance of $F(X_{(i)})$?
[edit: I have realised by now that my approach was flawed and furthermore that my question needs clarification. So here is my second try.]
As I understand the question, we have real valued, i.i.d. random variables $X_1$, ..., $X_n$ with density $f$ and CDF $F$.
$X_{(1)}< ... < X_{(n)}$ are the corresponding order statistics with CDF $F_i:=F_{X_{(i)}}$.
I know the general formula for the CDF of order statistics. It is given by $$F_i(t)=\sum_{k=i}^n \binom{n}{k}F(t)^k(1-F(t))^{n-k}.$$
Now, the CDF of a random variable is a measurable function. Thus $F(X_{(i)})$ and $F_i(X_{(i)})$ are real valued random variables again. And a) asks for their distribution.
Per definition, we have $$F(t)=\mathbb{P}(X_i\leq t).$$ Hence $$F(X_{(i)})=\mathbb{P}(X_i\leq X_{(i)}).$$ This is the probability of the event that any $X_i$ is less or equal $X_{(i)}$. By definition of order statistics, we have $$F(X_{(n)})=\mathbb{P}(X_i\leq X_{(n)})=1,$$ as $X_{(n)}$ is the maximum of the $X_i$. But how do I derive the distribution of $F(X_{(i)})$ for $i \in \{1, ..., n-1\}$?
I think, if they were uniformly distributed, the answer would simply be $$F(X_{(i)})=i/n.$$ But their distribution ist unknown, so I'm stuck and have no idea how to proceed from here.