If I've understood correctly, the Benjamini-Hochberg (BH) correction is used to correct for the rate of false discoveries (FDR) when testing a collection of $m$ random variables, $\{X_1, \ldots,X_m\}$ against $m$ null hypotheses $\{H_0^1, \ldots, H_0^m\}$, of which $k\leq m$ can be true.
Now consider a situation where you have a set of random variables that are sorted. For example, $\{Y_1,\ldots,Y_m\}$ with $Y_m\leq\ldots \leq Y_1$ and each $Y_i\sim F_i$ (i.e., distributed according to $F_i$). An example of such a scenario would be the eigenvalues of a random matrix. Suppose now, that given a single null hypothesis $H_0$, and test statistic $T_i$, the $Y_i$'s are tested in descending order — i.e., $Y_1$, then $Y_2$, and so on, until $H_0$ is false.
Is the BH correction also applicable here or is it a fundamentally different scenario? Or does the question of controlling FDR not arise at all?