  Consider a Poisson point process with unknown support boundary curve $g$,
which forms a prototype of an irregular statistical model. We address the
problem of estimating non-linear functionals of the form $\int \Phi(g(x))\,dx$.
Following a nonparametric maximum-likelihood approach, we construct an
estimator which is UMVU over H\"older balls and achieves the (local) minimax
rate of convergence. These results hold under weak assumptions on $\Phi$ which
are satisfied for $\Phi(u)=|u|^p$, $p\ge 1$. As an application, we consider the
problem of estimating the $L^p$-norm and derive the minimax separation rates in
the corresponding nonparametric hypothesis testing problem. Structural
differences to results for regular nonparametric models are discussed.
