Sparse signal recovery under poisson statistics for online marketing applications

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.

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