Synopsis
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.
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Review
`This is an impressive book ... of high mathematical quality and is written in a format with theorems and proofs. Each chapter ends with bibliographical notes, complements and exercises. The latter make the book also interesting for teaching graduate courses.'
Publication of the International Statistical Institute
`Overall, the topics included cover a broad spectrum of processes and inference methods in a fairly comprehensive manner and in depth. The book is highly recommended reading.'
Mathematical Reviews, 2002b
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Taschenbuch. Condition: Neu. Stochastic Processes | Inference Theory | Malempati M. Rao | Taschenbuch | Englisch | 2010 | Springer US | EAN 9781441948328 | Verantwortliche Person für die EU: Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, buchhandel-buch[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 107252131
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