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, covering a large number of frequently applied stochastic process models with discrete as well as continuous time. To make the reading even 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 later. Most of the concepts and tools from stochastic calculus needed when working with inference for stochastic processes are introduced and explained without proof in an appendix. This appendix can also be used independently as an introduction to stochastic calculus for statisticians. Numerous exercises are also included.

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, covering a large number of frequently applied stochastic process models with discrete as well as continuous time. To make the reading even 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 later. Most of the concepts and tools from stochastic calculus needed when working with inference for stochastic processes are introduced and explained without proof in an appendix. This appendix can also be used independently as an introduction to stochastic calculus for statisticians. Numerous exercises are also included.

Dieses Buch vermittelt ein solides Grundwissen uber Masstheorie, indem es die wichtigsten Teile derselben in detaillierten, gut nachvollziehbaren Schritten darlegt sowie mit zahlreichen Beispielen verbindet. Viele UEbungsaufgaben unterschiedlicher Schwierigkeitsgrade unterstutzen dabei das Verstandnis des Stoffes. Zur Selbstkontrolle werden im Anhang Loesungen zu samtlichen UEbungsaufgaben angegeben. Anwendungen der Masstheorie in der Stochastik werden in Kapiteln uber bedingte Erwartungen und Likelihood-Funktionen aufgezeigt. Die benoetigten Vorkenntnisse sind auf ein Minimum beschrankt, da zu Beginn in ubersichtlicher Form notwendige Grundlagen aus Mengenlehre und Theorie der reellen Zahlen wiederholt und vertieft werden.