Statistical Inference for Stochastic Processes

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Academic Press, 1980 - Mathematics - 435 pages
The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects of stochastic processes. This is a research monograph written for specialists in the common area of stochastic processes and theoretical statistics. The topics in the book have been divided into three parts. Part I presents an introduction and discusses some standard models. In this part, main ideas and methods are emphasized, avoiding technicalities as far as possible. Part II consists of three chapters on the theory of inference for general processes in discrete and continuous time including diffusion processes. Part III surveys in three chapters recent results on Bayesian, non-parametric and sequential procedures. The treatment of the subject in Parts II and III is more rigorous than that in Part I, and the theory is emphasized here rather than the methods.

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Contents

Introductory Examples of Stochastic Models Example 1 A Random Walk Model for Neuron Firing
1
Chain Binomial Models in Epidemiology
2
A Population Growth Model
3
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