Statistical Inference for Stochastic Processes

Statistical Inference Stochastic Processes provides information pertinent to the theory of stochastic processes. This book discusses stochastic models that are increasingly used in scientific research and describes some of their applications. Organized into three parts encompassing 12 chapters, this book begins with an overview of the basic concepts and procedures of statistical inference. This text then explains the inference problems for Galton–Watson process for discrete time and Markov-branching processes for continuous time. Other chapters consider problems of prediction, filtering, and parameter estimation for some simple discrete-time linear stochastic processes. This book discusses as well the ergodic type chains with finite and countable state-spaces and describes some results on birth and death processes that are of a non-ergodic type. The final chapter deals with inference procedures for stochastic processes through sequential procedures. This book is a valuable resource for graduate students.

Emeritus Professor, Department of Statistics, University of Georgia, Athens, GA, USA

B.L.S. Prakasa Rao Ph.D, FNA, FASc, FNASc, FAPAS is Professor Emeritus and former Director, Indian Statistical Institute, University of Hyderabad Campus, Hyderabad, India