Stochastic Processes : Inference Theory

M. M. Rao

ISBN 10: 0792363248 / ISBN 13: 9780792363248

Editore: Springer, Berlin, Springer US Mai 2000, 2000

Riguardo questo articolo

Neuware - The material accumulated and presented in this volume can be ex plained easily. At the start of my graduate studies in the early 1950s, I Grenander's (1950) thesis, and was much attracted to the came across entire subject considered there. I then began preparing for the neces sary mathematics to appreciate and possibly make some contributions to the area. Thus after a decade of learning and some publications on the way, I wanted to write a modest monograph complementing Grenander's fundamental memoir. So I took a sabbatical leave from my teaching position at the Carnegie-Mellon University, encouraged by an Air Force Grant for the purpose, and followed by a couple of years more learning opportunity at the Institute for Advanced Study to complete the project. As I progressed, the plan grew larger needing a substantial background material which was made into an independent initial volume in (1979). In its preface I said: 'My intension was to present the following material as the first part of a book treating the In ference Theory of stochastic processes, but the latter account has now receded to a distant future,' namely for two more decades! Meanwhile, a much enlarged second edition of that early work has appeared (1995), and now I am able to present the main part of the original plan.

Codice articolo 9780792363248

Dati bibliografici

• Titolo: Stochastic Processes : Inference Theory
• Editore: Springer, Berlin, Springer US Mai 2000
• Data di pubblicazione: 2000
• Lingua: Inglese
• ISBN 10: 0792363248
• ISBN 13: 9780792363248
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 240 L x 160 A x 36 P (millimetri) 240 millimetri di larghezza per 160 millimetri di altezza per 36 millimetri di profondità
• Peso: 1107 grammi

Descrizione articolo

Riassunto

This volume presents a mathematical treatment of classical inference theory (Neyman-Pearson, Fisher and Wald) from the perspective of using it in stochastic processes, including some generalizations. It includes analysis of likelihood ratios for both Gaussian and several other classes (infinitely divisible, jump Markov, diffusion and additive). Both linear and non-linear filtering (also for general non-quadratic 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 also included. The text is designed to be useful to researchers and graduate students working in mathematics, statistics, and systems and communication engineering.

Recensione

`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