Stochastic Processes

Inference Theory

AvMalempati M. Rao

Inbunden, Engelska, 2000

Del i serien Mathematics and Its Applications

2 803 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2000-05-31
Mått:156 x 234 x 43 mm
Vikt:1 156 g
Format:Inbunden
Språk:Engelska
Serie:Mathematics and Its Applications
Antal sidor:645
Upplaga:2000
Förlag:Kluwer Academic Publishers
ISBN:9780792363248

Utforska kategorier

Recensioner i media

'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

Innehållsförteckning

I: Introduction and Preliminaries.- II: Some Principles of Hypothesis Testing.- III: Parameter Estimation and Asymptotics.- IV: Inferences for Classes of Processes.- V: Likelihood Ratios for Processes.- VI: Sampling Methods for Processes.- VII: More on Stochastic Inference.- VIII: Prediction and Filtering of Processes.- IX: Nonparametric Estimation for Processes.- Notation index.- Author index.