  Let $X_1,...,X_n$ be iid random vectors and $f\ge 0$ be a non-negative
function. Let also $k(n) = {\rm Argmax}_{i=1,...,n} f(X_i)$. We are interested
in the distribution of $X_{k(n)}$ and their limit theorems. In other words,
what is the distribution the random vector where a function of its components
is extreme? This question is motivated by a kind of inverse problem where one
wants to determine the extremal behavior of $X$ when only explicitly observing
$f(X)$. We shall refer to such types of results as to implicit extremes. It
turns out that, as in the usual case of explicit extremes, all limit implicit
extreme value laws are implicit max-stable. We characterize the regularly
varying implicit max-stable laws in terms of their spectral and stochastic
representations. We also establish the asymptotic behavior of implicit order
statistics relative to a given homogeneous loss and conclude with several
examples drawing connections to prior work involving regular variation on
general cones.
