Stochastic Processes Inference Theory
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Stochastic Processes Inference Theory
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Author : Malempati M. Rao
language : en
Publisher: Springer
Release Date : 2014-11-14
Stochastic Processes Inference Theory written by Malempati M. Rao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-14 with Mathematics categories.
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Theory Of Stochastic Objects
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Author : Athanasios Christou Micheas
language : en
Publisher: CRC Press
Release Date : 2018-01-19
Theory Of Stochastic Objects written by Athanasios Christou Micheas and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-19 with Mathematics categories.
This book defines and investigates the concept of a random object. To accomplish this task in a natural way, it brings together three major areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by existing textbooks; one would need material on real analysis, measure and probability theory, as well as stochastic processes - in addition to at least one text on statistics- to capture the detail and depth of material that has gone into this volume. Presents and illustrates ‘random objects’ in different contexts, under a unified framework, starting with rudimentary results on random variables and random sequences, all the way up to stochastic partial differential equations. Reviews rudimentary probability and introduces statistical inference, from basic to advanced, thus making the transition from basic statistical modeling and estimation to advanced topics more natural and concrete. Compact and comprehensive presentation of the material that will be useful to a reader from the mathematics and statistical sciences, at any stage of their career, either as a graduate student, an instructor, or an academician conducting research and requiring quick references and examples to classic topics. Includes 378 exercises, with the solutions manual available on the book's website. 121 illustrative examples of the concepts presented in the text (many including multiple items in a single example). The book is targeted towards students at the master’s and Ph.D. levels, as well as, academicians in the mathematics, statistics and related disciplines. Basic knowledge of calculus and matrix algebra is required. Prior knowledge of probability or measure theory is welcomed but not necessary.
Bayesian Analysis Of Stochastic Process Models
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Author : David Insua
language : en
Publisher: John Wiley & Sons
Release Date : 2012-05-07
Bayesian Analysis Of Stochastic Process Models written by David Insua and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-07 with Mathematics categories.
Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
Advances In Statistical Inference For Processes Driven By Fractional Processes Inference For Fractional Processes
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Author : B L S Prakasa Rao
language : en
Publisher: World Scientific
Release Date : 2025-07-08
Advances In Statistical Inference For Processes Driven By Fractional Processes Inference For Fractional Processes written by B L S Prakasa Rao and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-08 with Mathematics categories.
One of the important problems in studying stochastic phenomena is to develop stochastic models and understand their implications behind the phenomenon. Long range dependence is an important stochastic phenomena and it needs study of special type of stochastic processes for modelling. My earlier book on Statistical Inference for Fractional Diffusion Processes (2010) dealt with several aspects for modelling by fractional Brownian motion. This book will contain my work on parametric and nonparametric inference for processes driven by fractional processes such as fractional Brownian motion, mixed fractional Brownian motion, sub-fractional Brownian motion, alpha-stable noise, fractional Levy process and Gaussian processes.
A Course In Stochastic Processes
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Author : Denis Bosq
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
A Course In Stochastic Processes written by Denis Bosq and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
This text is an Elementary Introduction to Stochastic Processes in discrete and continuous time with an initiation of the statistical inference. The material is standard and classical for a first course in Stochastic Processes at the senior/graduate level (lessons 1-12). To provide students with a view of statistics of stochastic processes, three lessons (13-15) were added. These lessons can be either optional or serve as an introduction to statistical inference with dependent observations. Several points of this text need to be elaborated, (1) The pedagogy is somewhat obvious. Since this text is designed for a one semester course, each lesson can be covered in one week or so. Having in mind a mixed audience of students from different departments (Math ematics, Statistics, Economics, Engineering, etc.) we have presented the material in each lesson in the most simple way, with emphasis on moti vation of concepts, aspects of applications and computational procedures. Basically, we try to explain to beginners questions such as "What is the topic in this lesson?" "Why this topic?", "How to study this topic math ematically?". The exercises at the end of each lesson will deepen the stu dents' understanding of the material, and test their ability to carry out basic computations. Exercises with an asterisk are optional (difficult) and might not be suitable for homework, but should provide food for thought.
Statistical Inference In Stochastic Processes
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Author : Ishwar V. Basawa
language : en
Publisher:
Release Date : 1994
Statistical Inference In Stochastic Processes written by Ishwar V. Basawa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.
Statistical Inference For Fractional Diffusion Processes
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Author : B. L. S. Prakasa Rao
language : en
Publisher: John Wiley & Sons
Release Date : 2011-07-05
Statistical Inference For Fractional Diffusion Processes written by B. L. S. Prakasa Rao and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-05 with Mathematics categories.
Stochastic processes are widely used for model building in the social, physical, engineering and life sciences as well as in financial economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications point of view. This book deals with Fractional Diffusion Processes and statistical inference for such stochastic processes. The main focus of the book is to consider parametric and nonparametric inference problems for fractional diffusion processes when a complete path of the process over a finite interval is observable. Key features: Introduces self-similar processes, fractional Brownian motion and stochastic integration with respect to fractional Brownian motion. Provides a comprehensive review of statistical inference for processes driven by fractional Brownian motion for modelling long range dependence. Presents a study of parametric and nonparametric inference problems for the fractional diffusion process. Discusses the fractional Brownian sheet and infinite dimensional fractional Brownian motion. Includes recent results and developments in the area of statistical inference of fractional diffusion processes. Researchers and students working on the statistics of fractional diffusion processes and applied mathematicians and statisticians involved in stochastic process modelling will benefit from this book.
Statistical Inference From Stochastic Processes
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Author : Narahari Umanath Prabhu
language : en
Publisher: American Mathematical Soc.
Release Date : 1988
Statistical Inference From Stochastic Processes written by Narahari Umanath Prabhu and has been published by American Mathematical Soc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Mathematics categories.
Comprises the proceedings of the AMS-IMS-SIAM Summer Research Conference on Statistical Inference from Stochastic Processes, held at Cornell University in August 1987. This book provides students and researchers with a familiarity with the foundations of inference from stochastic processes and intends to provide a knowledge of the developments.
Statistical Inferences For Stochasic Processes
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Author : Ishwar V. Basawa
language : en
Publisher: Academic Press
Release Date : 1980-01-28
Statistical Inferences For Stochasic Processes written by Ishwar V. Basawa and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980-01-28 with Mathematics categories.
Introductory examples of stochastic models; Special models; General theory; Further approaches.
Foundations Of Probability Theory
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Author : Himadri Deshpande
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Foundations Of Probability Theory written by Himadri Deshpande and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Science categories.
"Foundations of Probability Theory" offers a thorough exploration of probability theory's principles, methods, and applications. Designed for students, researchers, and practitioners, this comprehensive guide covers both foundational concepts and advanced topics. We begin with basic probability concepts, including sample spaces, events, probability distributions, and random variables, progressing to advanced topics like conditional probability, Bayes' theorem, and stochastic processes. This approach lays a solid foundation for further exploration. Our book balances theory and application, emphasizing practical applications and real-world examples. We cover topics such as statistical inference, estimation, hypothesis testing, Bayesian inference, Markov chains, Monte Carlo methods, and more. Each topic includes clear explanations, illustrative examples, and exercises to reinforce learning. Whether you're a student building a solid understanding of probability theory, a researcher exploring advanced topics, or a practitioner applying probabilistic methods to solve real-world problems, this book is an invaluable resource. We equip readers with the knowledge and tools necessary to tackle complex problems, make informed decisions, and explore probability theory's rich landscape with confidence.