Statistical Inference In Stochastic Processes

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Statistical Inference from Stochastic Processes

Author : Ams-Ims-Siam Joint Summer Research Conference in the Mathematical Scie
Publisher : American Mathematical Soc.
ISBN 13 : 0821850873
Total Pages : 386 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Statistical Inference from Stochastic Processes by : Ams-Ims-Siam Joint Summer Research Conference in the Mathematical Scie

Download or read book Statistical Inference from Stochastic Processes written by Ams-Ims-Siam Joint Summer Research Conference in the Mathematical Scie and published by American Mathematical Soc.. This book was released on 1988 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume comprises the proceedings of the AMS-IMS-SIAM Summer Research Conference on Statistical Inference from Stochastic Processes, held at Cornell University in August 1987. The conference brought together probabilists and statisticians who have developed important areas of application and made major contributions to the foundations of the subject. Statistical inference from stochastic processes has been important in a number of areas. For example, in applied probability, major advances have been made in recent years in stochastic models arising in science and engineering. However, the emphasis has been on the formulation and analysis of models rather than on the statistical methodology for hypothesis testing and inference. For these models to be of practical use, procedures for their statistical analysis are essential. In the area of probability models, initial work in inference focused on Markov chains, but many models have given rise to non-Markovian and point processes. In recent years, research in statistical inference from such processes not only solved specific problems but also resulted in major contributions to the conceptual framework of the subject as well as the associated techniques. The objective of the conference was to provide the opportunity to survey and evaluate the current state of the art in this area and to discuss future directions. The papers presented covered five topics within the broad domain of inference from stochastic processes: foundations, counting processes and survival analysis, likelihood and its ramifications, applications to statistics and probability models, and processes in economics. Requiring a graduate level background in probability and statistical inference, this book will provide students and researchers with a familiarity with the foundations of inference from stochastic processes and a knowledge of the current developments in this area.

Statistical Inference for Discrete Time Stochastic Processes

Author : M. B. Rajarshi
Publisher : Springer Science & Business Media
ISBN 13 : 8132207629
Total Pages : 121 pages
Book Rating : 4.1/5 (322 download)

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Book Synopsis Statistical Inference for Discrete Time Stochastic Processes by : M. B. Rajarshi

Download or read book Statistical Inference for Discrete Time Stochastic Processes written by M. B. Rajarshi and published by Springer Science & Business Media. This book was released on 2012-10-05 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.

Statistical Inference in Stochastic Processes

Author : N.U. Prabhu
Publisher : CRC Press
ISBN 13 : 1000147746
Total Pages : 294 pages
Book Rating : 4.0/5 (1 download)

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Book Synopsis Statistical Inference in Stochastic Processes by : N.U. Prabhu

Download or read book Statistical Inference in Stochastic Processes written by N.U. Prabhu and published by CRC Press. This book was released on 2020-08-13 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering both theory and applications, this collection of eleven contributed papers surveys the role of probabilistic models and statistical techniques in image analysis and processing, develops likelihood methods for inference about parameters that determine the drift and the jump mechanism of a di

Statistical Inferences for Stochasic Processes

Author : Ishwar V. Basawa
Publisher : Academic Press
ISBN 13 :
Total Pages : 464 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Statistical Inferences for Stochasic Processes by : Ishwar V. Basawa

Download or read book Statistical Inferences for Stochasic Processes written by Ishwar V. Basawa and published by Academic Press. This book was released on 1980-01-28 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introductory examples of stochastic models; Special models; General theory; Further approaches.

Bayesian Inference for Stochastic Processes

Author : Lyle D. Broemeling
Publisher : CRC Press
ISBN 13 : 1315303574
Total Pages : 373 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Bayesian Inference for Stochastic Processes by : Lyle D. Broemeling

Download or read book Bayesian Inference for Stochastic Processes written by Lyle D. Broemeling and published by CRC Press. This book was released on 2017-12-12 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.

Statistical Inference for Ergodic Diffusion Processes

Author : Yury A. Kutoyants
Publisher : Springer Science & Business Media
ISBN 13 : 144713866X
Total Pages : 493 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Statistical Inference for Ergodic Diffusion Processes by : Yury A. Kutoyants

Download or read book Statistical Inference for Ergodic Diffusion Processes written by Yury A. Kutoyants and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.

Semimartingales and their Statistical Inference

Author : B.L.S. Prakasa Rao
Publisher : CRC Press
ISBN 13 : 9781584880080
Total Pages : 684 pages
Book Rating : 4.8/5 (8 download)

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Book Synopsis Semimartingales and their Statistical Inference by : B.L.S. Prakasa Rao

Download or read book Semimartingales and their Statistical Inference written by B.L.S. Prakasa Rao and published by CRC Press. This book was released on 1999-05-11 with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical inference carries great significance in model building from both the theoretical and the applications points of view. Its applications to engineering and economic systems, financial economics, and the biological and medical sciences have made statistical inference for stochastic processes a well-recognized and important branch of statistics and probability. The class of semimartingales includes a large class of stochastic processes, including diffusion type processes, point processes, and diffusion type processes with jumps, widely used for stochastic modeling. Until now, however, researchers have had no single reference that collected the research conducted on the asymptotic theory for semimartingales. Semimartingales and their Statistical Inference, fills this need by presenting a comprehensive discussion of the asymptotic theory of semimartingales at a level needed for researchers working in the area of statistical inference for stochastic processes. The author brings together into one volume the state-of-the-art in the inferential aspect for such processes. The topics discussed include: Asymptotic likelihood theory Quasi-likelihood Likelihood and efficiency Inference for counting processes Inference for semimartingale regression models The author addresses a number of stochastic modeling applications from engineering, economic systems, financial economics, and medical sciences. He also includes some of the new and challenging statistical and probabilistic problems facing today's active researchers working in the area of inference for stochastic processes.

Statistical Analysis of Stochastic Processes in Time

Author : J. K. Lindsey
Publisher : Cambridge University Press
ISBN 13 : 9781139454513
Total Pages : 356 pages
Book Rating : 4.4/5 (545 download)

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Book Synopsis Statistical Analysis of Stochastic Processes in Time by : J. K. Lindsey

Download or read book Statistical Analysis of Stochastic Processes in Time written by J. K. Lindsey and published by Cambridge University Press. This book was released on 2004-08-02 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying stochastic processes to an audience knowledgeable only in basic statistics. It covers almost all aspects of the subject and presents the theory in an easily accessible form that is highlighted by application to many examples. These examples arise from dozens of areas, from sociology through medicine to engineering. Complementing these are exercise sets making the book suited for introductory courses in stochastic processes. Software (available from www.cambridge.org) is provided for the freely available R system for the reader to apply to all the models presented.

Simulation and Inference for Stochastic Processes with YUIMA

Author : Stefano M. Iacus
Publisher : Springer
ISBN 13 : 3319555693
Total Pages : 268 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Simulation and Inference for Stochastic Processes with YUIMA by : Stefano M. Iacus

Download or read book Simulation and Inference for Stochastic Processes with YUIMA written by Stefano M. Iacus and published by Springer. This book was released on 2018-06-01 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA package, available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page.

Probability, Statistics, and Stochastic Processes

Author : Peter Olofsson
Publisher : John Wiley & Sons
ISBN 13 : 0470889748
Total Pages : 573 pages
Book Rating : 4.4/5 (78 download)

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Book Synopsis Probability, Statistics, and Stochastic Processes by : Peter Olofsson

Download or read book Probability, Statistics, and Stochastic Processes written by Peter Olofsson and published by John Wiley & Sons. This book was released on 2012-05-22 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition ". . . an excellent textbook . . . well organized and neatly written." —Mathematical Reviews ". . . amazingly interesting . . ." —Technometrics Thoroughly updated to showcase the interrelationships between probability, statistics, and stochastic processes, Probability, Statistics, and Stochastic Processes, Second Edition prepares readers to collect, analyze, and characterize data in their chosen fields. Beginning with three chapters that develop probability theory and introduce the axioms of probability, random variables, and joint distributions, the book goes on to present limit theorems and simulation. The authors combine a rigorous, calculus-based development of theory with an intuitive approach that appeals to readers' sense of reason and logic. Including more than 400 examples that help illustrate concepts and theory, the Second Edition features new material on statistical inference and a wealth of newly added topics, including: Consistency of point estimators Large sample theory Bootstrap simulation Multiple hypothesis testing Fisher's exact test and Kolmogorov-Smirnov test Martingales, renewal processes, and Brownian motion One-way analysis of variance and the general linear model Extensively class-tested to ensure an accessible presentation, Probability, Statistics, and Stochastic Processes, Second Edition is an excellent book for courses on probability and statistics at the upper-undergraduate level. The book is also an ideal resource for scientists and engineers in the fields of statistics, mathematics, industrial management, and engineering.

Asymptotic Theory of Statistical Inference for Time Series

Author : Masanobu Taniguchi
Publisher : Springer Science & Business Media
ISBN 13 : 146121162X
Total Pages : 671 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Asymptotic Theory of Statistical Inference for Time Series by : Masanobu Taniguchi

Download or read book Asymptotic Theory of Statistical Inference for Time Series written by Masanobu Taniguchi and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA, and ARMA processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss estimation and testing theory and many other relevant statistical methods and techniques.

Statistical Inference for Stochastic Processes

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (641 download)

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Book Synopsis Statistical Inference for Stochastic Processes by :

Download or read book Statistical Inference for Stochastic Processes written by and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Essentials of Stochastic Processes

Author : Richard Durrett
Publisher : Springer
ISBN 13 : 3319456148
Total Pages : 282 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Essentials of Stochastic Processes by : Richard Durrett

Download or read book Essentials of Stochastic Processes written by Richard Durrett and published by Springer. This book was released on 2016-11-07 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader’s understanding. Drawing from teaching experience and student feedback, there are many new examples and problems with solutions that use TI-83 to eliminate the tedious details of solving linear equations by hand, and the collection of exercises is much improved, with many more biological examples. Originally included in previous editions, material too advanced for this first course in stochastic processes has been eliminated while treatment of other topics useful for applications has been expanded. In addition, the ordering of topics has been improved; for example, the difficult subject of martingales is delayed until its usefulness can be applied in the treatment of mathematical finance.

Statistical Inference and Related Topics

Author : Madan Lal Puri
Publisher : Academic Press
ISBN 13 : 1483257606
Total Pages : 364 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Statistical Inference and Related Topics by : Madan Lal Puri

Download or read book Statistical Inference and Related Topics written by Madan Lal Puri and published by Academic Press. This book was released on 2014-05-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Inference and Related Topics, Volume 2 presents the proceedings of the Summer Research Institute on Statistical Inference for Stochastic Processes, held in Bloomingdale, Indiana on July 31 to August 9, 1975. This book focuses on the theory of statistical inference for stochastic processes. Organized into 15 chapters, this volume begins with an overview of the case of continuous distributions with one real parameter. This text then reviews some results for multidimensional empirical processes and Brownian sheets when they are indexed by families of sets. Other chapters consider a class of cubic spline estimators of probability density functions over a finite interval. This book discusses as well the method to construct nonelimination type sequential procedures to select a subset containing all the superior populations. The final chapter deals with Markov sequences, which are among the most interesting available for study with a rich theory and varied applications. This book is a valuable resource for graduate students and research workers.

Bayesian Analysis of Stochastic Process Models

Author : David Insua
Publisher : John Wiley & Sons
ISBN 13 : 1118304039
Total Pages : 315 pages
Book Rating : 4.1/5 (183 download)

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Book Synopsis Bayesian Analysis of Stochastic Process Models by : David Insua

Download or read book Bayesian Analysis of Stochastic Process Models written by David Insua and published by John Wiley & Sons. This book was released on 2012-04-02 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

A Course in Stochastic Processes

Author : Denis Bosq
Publisher : Springer Science & Business Media
ISBN 13 : 9401587698
Total Pages : 355 pages
Book Rating : 4.4/5 (15 download)

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Book Synopsis A Course in Stochastic Processes by : Denis Bosq

Download or read book A Course in Stochastic Processes written by Denis Bosq and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 for Discrete Time Stochastic Processes

Author : M. B. Rajarshi
Publisher : Springer Science & Business Media
ISBN 13 : 8132207637
Total Pages : 113 pages
Book Rating : 4.1/5 (322 download)

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Book Synopsis Statistical Inference for Discrete Time Stochastic Processes by : M. B. Rajarshi

Download or read book Statistical Inference for Discrete Time Stochastic Processes written by M. B. Rajarshi and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.