STOCHASTIC PROCESSES - INFERENCE THEORY

This book presents a complete mathematical treatment of classical inference theory (Neyman-Pearson, Fisher, and Wald) from the point of using it in stochastic processes, including some generalizations. It includes detailed analysis of likelihood ratios for both Gaussian and several other classes (infinitely divisible, jump Markov, diffusion and additive). Both linear and nonlinear filtering (also for general nonquadratic criteria) are treated. The corresponding Kalman-Bucy filters for continuous parameter processes are presented. Consistency and limit distributions of estimations of biospectral densities of harmonizable processes are given.
Audience: Researchers and graduate students working in mathematics, statistics, and systems and communication engineering.