Sparse signal recovery under poisson statistics for online marketing applications
Delaram Motamedvaziri, Mohammad Hossein Rohban, Venkatesh Saligrama · 2014
We are motivated by many applications such as problems that arise in online marketing applications, where the observations are governed by non-homogeneous Poisson models. We analyze the performance of a Maximum Likelihood (ML) decoder. We prove consistency and show an exponential rate of converge for sparse recovery in the high-dimensional Poisson setting. After verifying the efficiency of ML estimator empirically, we apply the ML decoder to study the dynamics of online marketing methods over time.