Statistical Inference for Stochastic Processes. Theory and Methods

Content: 
This is a volume in PROBABILITY AND MATHEMATICAL STATISTICS, Page ii
Front Matter, Page iii
Copyright, Page iv
Preface, Pages xi-xii
List of Notation, Pages xiii-xiv
Dedication, Page xv
Chapter 0 - Introductory Examples of Stochastic Models, Pages 1-9
Chapter 1 - Basic Principles and Methods of Statistical Inference, Pages 10-19
Chapter 2 - Branching Processes, Pages 20-34
Chapter 3 - Simple Linear Models, Pages 35-51
Chapter 4 - Discrete Markov Chains, Pages 52-80
Chapter 5 - Markov Chains in Continuous Time, Pages 81-97
Chapter 6 - Simple Point Processes, Pages 98-118
Chapter 7 - Large Sample Theory for Discrete Parameter Stochastic Processes, Pages 119-165
Chapter 8 - Large Sample Theory for Continuous Parameter Stochastic Processes, Pages 166-200
Chapter 9 - Diffusion Processes, Pages 201-254
Chapter 10 - Bayesian Inference for Stochastic Processes, Pages 255-293
Chapter 11 - Nonparametric Inference for Stochastic Processes, Pages 294-342
Chapter 12 - Sequential Inference for Stochastic Processes, Pages 343-382
Appendix 1 - Martingales, Pages 383-395
Appendix 2 - Stochastic Differential Equations, Pages 396-410
Appendix 3 - Proof of Sudakov's Lemma (Theorem 2.3 of Chapter 12), Pages 411-412
Appendix 4 - Generalized Functions and Generalized Stochastic Processes, Pages 413-414
References, Pages 415-431
Index, Pages 433-435
Probability and Mathematical Statistics, Pages ibc1-ibc2