Description
In this talk, we consider several ways of testing the mean of a Gaussian process with a special focus on their power against sparse'' alternatives. This investigation depicts the limit of the approaches based on evaluation of the process of very thin grids (even when joint with techniques inspired L1 recovery). A contrario,gridless'' methods accounting for the variation of the process (through the Hessian), are presented and seem to empirically outperform grid-based methods.
References: Testing Gaussian Process with Applications to Super-Resolution (with J.-M. Azaïs and S. Mourareau), arXiv 1706.00679.