Anthony Réveillac (INSA Toulouse)
Hawkes processes have proved to be a powerful probabilistic model for various applications in neurosciences or insurance. These counting processes are defined through their intensity which is...mehr erfahren
Gregor Pasemann (HU)
We consider the problem of estimating the diffusivity of a stochastic heat equation (or more generally, the parametrized drift of an abstract linear stochastic parabolic evolution equation) from spectral or local measurements perturbed by small observation noise. Using a kernel smoothing approach, we construct a modified maximum likelihood estimator and study its asymptotic properties. In particular, we find an optimal tradeoff between exploiting small-scale spatial information and averaging out the observation noise.
This talk is based on work in progress with Markus Reiß.