Statistical Inference In Stochastic Processes

Author : Ams-Ims-Siam Joint Summer Research Conference in the Mathematical Scie,Narahari Umanath Prabhu,Joint Summer Research Conference in the Mathematical Sciences on Statistical Inference from Stochastic Processes (1987, Ithaca, NY),AMS-IMS-SIAM JOINT SUMMER RESEARCH CONFERENCE IN T
Publisher : American Mathematical Soc.
Page : 386 pages
File Size : 50,7 Mb
Release : 1988
Category : Mathematics
ISBN : 9780821850879

Get Book

Statistical Inference from Stochastic Processes by Ams-Ims-Siam Joint Summer Research Conference in the Mathematical Scie,Narahari Umanath Prabhu,Joint Summer Research Conference in the Mathematical Sciences on Statistical Inference from Stochastic Processes (1987, Ithaca, NY),AMS-IMS-SIAM JOINT SUMMER RESEARCH CONFERENCE IN T Pdf

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.

Author : N.U. Prabhu
Publisher : CRC Press
Page : 294 pages
File Size : 53,7 Mb
Release : 2020-08-13
Category : Mathematics
ISBN : 9781000147742

Get Book

Statistical Inference in Stochastic Processes by N.U. Prabhu Pdf

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

Author : Lyle D. Broemeling
Publisher : CRC Press
Page : 373 pages
File Size : 44,8 Mb
Release : 2017-12-12
Category : Mathematics
ISBN : 9781315303574

Get Book

Bayesian Inference for Stochastic Processes by Lyle D. Broemeling Pdf

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.

Author : Ishwar V. Basawa,B. L. S. Prakasa Rao
Publisher : Academic Press
Page : 464 pages
File Size : 41,5 Mb
Release : 1980-01-28
Category : Mathematics
ISBN : UOM:39015006420015

Get Book

Statistical Inferences for Stochasic Processes by Ishwar V. Basawa,B. L. S. Prakasa Rao Pdf

Introductory examples of stochastic models; Special models; General theory; Further approaches.

Author : Denis Bosq,Hung T. Nguyen
Publisher : Springer Science & Business Media
Page : 355 pages
File Size : 43,7 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9789401587693

Get Book

A Course in Stochastic Processes by Denis Bosq,Hung T. Nguyen Pdf

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.

Author : Madan Lal Puri
Publisher : Academic Press
Page : 365 pages
File Size : 42,7 Mb
Release : 2014-05-10
Category : Mathematics
ISBN : 9781483257600

Get Book

Statistical Inference and Related Topics by Madan Lal Puri Pdf

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.

Author : Peter Olofsson,Mikael Andersson
Publisher : John Wiley & Sons
Page : 573 pages
File Size : 40,5 Mb
Release : 2012-05-04
Category : Mathematics
ISBN : 9781118231326

Get Book

Probability, Statistics, and Stochastic Processes by Peter Olofsson,Mikael Andersson Pdf

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.

Author : Yury A. Kutoyants
Publisher : Springer Science & Business Media
Page : 493 pages
File Size : 47,6 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781447138662

Get Book

Statistical Inference for Ergodic Diffusion Processes by Yury A. Kutoyants Pdf

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.

Author : Alan Karr
Publisher : Routledge
Page : 509 pages
File Size : 48,9 Mb
Release : 2017-09-06
Category : Mathematics
ISBN : 9781351423830

Get Book

Point Processes and Their Statistical Inference by Alan Karr Pdf

Maintaining the excellent features that made the first edition so popular, this outstanding reference/text presents the only comprehensive treatment of the theory of point processes and statistical inference for point processes-highlighting both pointprocesses on the real line and sp;,.tial point processes. Thoroughly updated and revised to reflect changes since publication of the firstedition, the expanded Second EdiLion now contains a better organized and easierto-understand treatment of stationary point processes ... expanded treatment ofthe multiplicative intensity model ... expanded treatment of survival analysis . ..broadened consideration of applications ... an expanded and extended bibliographywith over 1,000 references ... and more than 3('() end-of-chapter exercises.

Author : M. B. Rajarshi
Publisher : Springer Science & Business Media
Page : 121 pages
File Size : 45,8 Mb
Release : 2012-10-05
Category : Mathematics
ISBN : 9788132207627

Get Book

Statistical Inference for Discrete Time Stochastic Processes by M. B. Rajarshi Pdf

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.

Author : B. L. S. Prakasa Rao
Publisher : John Wiley & Sons
Page : 213 pages
File Size : 53,7 Mb
Release : 2011-07-05
Category : Mathematics
ISBN : 9780470975763

Get Book

Statistical Inference for Fractional Diffusion Processes by B. L. S. Prakasa Rao Pdf

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.

Author : Ishwar V. Basawa
Publisher : Elsevier
Page : 455 pages
File Size : 54,5 Mb
Release : 2014-06-28
Category : Mathematics
ISBN : 9781483296142

Get Book

Statistical Inferences for Stochasic Processes by Ishwar V. Basawa Pdf

Stats Inference Stochasic Process

Author : M. B. Rajarshi
Publisher : Springer Science & Business Media
Page : 121 pages
File Size : 41,7 Mb
Release : 2014-07-08
Category : Mathematics
ISBN : 9788132207634

Get Book

Statistical Inference for Discrete Time Stochastic Processes by M. B. Rajarshi Pdf

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.

Author : B. L. S. Prakasa Rao,B. Ramdas Bhat
Publisher : Unknown
Page : 164 pages
File Size : 47,8 Mb
Release : 1996
Category : Probabilities
ISBN : 8122408362

Get Book

Stochastic Processes and Statistical Inference by B. L. S. Prakasa Rao,B. Ramdas Bhat Pdf

Author : B.L.S. Prakasa Rao
Publisher : CRC Press
Page : 684 pages
File Size : 55,7 Mb
Release : 1999-05-11
Category : Mathematics
ISBN : 1584880082

Get Book

Semimartingales and their Statistical Inference by B.L.S. Prakasa Rao Pdf

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.