Robust and Distributed Hypothesis Testing

This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL—divergence. Here, the distance can be chosen from a much general model, which includes the KL—divergence as a very special case. This is then extended by various means. A minimax robust test that is ...