Paper detail

Distribution of the Height of Local Maxima of Gaussian Random Fields

Let $\{f(t): t\in T\}$ be a smooth Gaussian random field over a parameter space $T$, where $T$ may be a subset of Euclidean space or, more generally, a Riemannian manifold. For any local maximum of $f(t)$ located at $t_0$ in the interior of $T$, we provide general formulae and asymptotic approximations for both the tail distribution of the height of a local maximum $\mathbb{P}\{f(t_0)>u | t_0 \text{is a local maximum of} f(t) \}$ and the overshoot distribution of a local maximum $\mathbb{P}\{f(t_0)>u+v | t_0 \text{is a local maximum of} f(t) \text{and} f(t_0)>v\}$. Assuming further that $f$ is isotropic, we apply techniques from random matrix theory related to the Gaussian orthogonal ensemble to compute such conditional probabilities explicitly when $T$ is Euclidean or a sphere of arbitrary dimension. Such calculations are motivated by the statistical problem of detecting peaks in the presence of smooth Gaussian noise.