Exponential Families of Stochastic Processes

Kuchler, Uwe; Srensen, Michael

ISBN 10: 038794981X / ISBN 13: 9780387949819

Published by Springer, 1997

About this Item

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Bibliographic Details

• Title: Exponential Families of Stochastic Processes
• Publisher: Springer
• Publication Date: 1997
• Language: English
• ISBN 10: 038794981X
• ISBN 13: 9780387949819
• Binding: Hardcover
• Condition: As New

About this title

Synopsis

Exponential families of stochastic processes are parametric stochastic p- cess models for which the likelihood function exists at all ?nite times and has an exponential representation where the dimension of the canonical statistic is ?nite and independent of time. This de?nition not only covers manypracticallyimportantstochasticprocessmodels,italsogivesrisetoa rather rich theory. This book aims at showing both aspects of exponential families of stochastic processes. Exponential families of stochastic processes are tractable from an a- lytical as well as a probabilistic point of view. Therefore, and because the theory covers many important models, they form a good starting point for an investigation of the statistics of stochastic processes and cast interesting light on basic inference problems for stochastic processes. Exponential models play a central role in classical statistical theory for independent observations, where it has often turned out to be informative and advantageous to view statistical problems from the general perspective of exponential families rather than studying individually speci?c expon- tial families of probability distributions. The same is true of stochastic process models. Thus several published results on the statistics of parti- lar process models can be presented in a uni?ed way within the framework of exponential families of stochastic processes.

Synopsis

This book provides a comprehensive account of the statistical theory of exponential families of stochastic processes. The book reviews the progress in the field made over the last ten years or so by the authors, two of the leading experts in the field, and several other researchers. The theory is applied to a broad spectrum of examples. A large number of frequently applied stochastic process models with discrete as well as with continuous time are covered by the theory developed in the book. The exponential families of stochastic processes are the most tractable type of statistical models for stochastic processes. On the other hand, they include models that are complex enough to exhibit basic inference problems that are peculiar to stochastic process models. Therefore they are a good starting point for the statistican who plans to work in this interesting and vigorous field. To make the reading easier for statisticians with only a basic background in the theory of stochastic process, the first part of the book is based on classical theory of stochastic processes only, while stochastic calculus is used late in the book.

Most of the concepts and tools from stochastic calculus that a statistician is likely to need, when working with inference for stochastic processes, are introduced and explained without proof in an appendix. The appendix can also be used independently as an introduction to stochastic calculus for statisticians. The statistical concepts are explained carefully so that probabilists with only a basic background in statistics can use the book to get into statistical inference for stochastic processes.