Maximum Likelihood Estimation for Stochastic Processes - a Martingale Approach

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Publisher :
ISBN 13 :
Total Pages : 222 pages
Book Rating : 4.:/5 (222 download)

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Book Synopsis Maximum Likelihood Estimation for Stochastic Processes - a Martingale Approach by : Paul David Feigin

Download or read book Maximum Likelihood Estimation for Stochastic Processes - a Martingale Approach written by Paul David Feigin and published by . This book was released on 1975 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Inferences for Stochasic Processes

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Publisher : Elsevier
ISBN 13 : 1483296148
Total Pages : 455 pages
Book Rating : 4.4/5 (832 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 Elsevier. This book was released on 2014-06-28 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stats Inference Stochasic Process

Martingale Limit Theory and Its Application

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Publisher : Academic Press
ISBN 13 : 1483263223
Total Pages : 321 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Martingale Limit Theory and Its Application by : P. Hall

Download or read book Martingale Limit Theory and Its Application written by P. Hall and published by Academic Press. This book was released on 2014-07-10 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Martingale Limit Theory and Its Application discusses the asymptotic properties of martingales, particularly as regards key prototype of probabilistic behavior that has wide applications. The book explains the thesis that martingale theory is central to probability theory, and also examines the relationships between martingales and processes embeddable in or approximated by Brownian motion. The text reviews the martingale convergence theorem, the classical limit theory and analogs, and the martingale limit theorems viewed as the rate of convergence results in the martingale convergence theorem. The book explains the square function inequalities, weak law of large numbers, as well as the strong law of large numbers. The text discusses the reverse martingales, martingale tail sums, the invariance principles in the central limit theorem, and also the law of the iterated logarithm. The book investigates the limit theory for stationary processes via corresponding results for approximating martingales and the estimation of parameters from stochastic processes. The text can be profitably used as a reference for mathematicians, advanced students, and professors of higher mathematics or statistics.

Statistical Estimation for Stochastic Processes

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Publisher :
ISBN 13 :
Total Pages : 272 pages
Book Rating : 4.3/5 (243 download)

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Book Synopsis Statistical Estimation for Stochastic Processes by : K. Nanthi

Download or read book Statistical Estimation for Stochastic Processes written by K. Nanthi and published by . This book was released on 1987 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Parameter Estimation in Stochastic Differential Equations

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Publisher : Springer
ISBN 13 : 3540744487
Total Pages : 271 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Parameter Estimation in Stochastic Differential Equations by : Jaya P. N. Bishwal

Download or read book Parameter Estimation in Stochastic Differential Equations written by Jaya P. N. Bishwal and published by Springer. This book was released on 2007-09-26 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.

Exponential Families of Stochastic Processes

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Publisher : Springer Science & Business Media
ISBN 13 : 0387227652
Total Pages : 325 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Exponential Families of Stochastic Processes by : Uwe Küchler

Download or read book Exponential Families of Stochastic Processes written by Uwe Küchler and published by Springer Science & Business Media. This book was released on 2006-05-09 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive account of the statistical theory of exponential families of stochastic processes. The book reviews the progress in the field made over the last ten years or so by the authors - two of the leading experts in the field - and several other researchers. The theory is applied to a broad spectrum of examples, covering a large number of frequently applied stochastic process models with discrete as well as continuous time. To make the reading even easier for statisticians with only a basic background in the theory of stochastic process, the first part of the book is based on classical theory of stochastic processes only, while stochastic calculus is used later. Most of the concepts and tools from stochastic calculus needed when working with inference for stochastic processes are introduced and explained without proof in an appendix. This appendix can also be used independently as an introduction to stochastic calculus for statisticians. Numerous exercises are also included.

Asymptotic Optimal Inference for Non-ergodic Models

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Publisher : Springer Science & Business Media
ISBN 13 : 1461255058
Total Pages : 183 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Asymptotic Optimal Inference for Non-ergodic Models by : I. V. Basawa

Download or read book Asymptotic Optimal Inference for Non-ergodic Models written by I. V. Basawa and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph contains a comprehensive account of the recent work of the authors and other workers on large sample optimal inference for non-ergodic models. The non-ergodic family of models can be viewed as an extension of the usual Fisher-Rao model for asymptotics, referred to here as an ergodic family. The main feature of a non-ergodic model is that the sample Fisher information, appropriately normed, converges to a non-degenerate random variable rather than to a constant. Mixture experiments, growth models such as birth processes, branching processes, etc. , and non-stationary diffusion processes are typical examples of non-ergodic models for which the usual asymptotics and the efficiency criteria of the Fisher-Rao-Wald type are not directly applicable. The new model necessitates a thorough review of both technical and qualitative aspects of the asymptotic theory. The general model studied includes both ergodic and non-ergodic families even though we emphasise applications of the latter type. The plan to write the monograph originally evolved through a series of lectures given by the first author in a graduate seminar course at Cornell University during the fall of 1978, and by the second author at the University of Munich during the fall of 1979. Further work during 1979-1981 on the topic has resolved many of the outstanding conceptual and technical difficulties encountered previously. While there are still some gaps remaining, it appears that the mainstream development in the area has now taken a more definite shape.

Stochastic Processes: Theory and Methods

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Publisher : Gulf Professional Publishing
ISBN 13 : 9780444500144
Total Pages : 990 pages
Book Rating : 4.5/5 (1 download)

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Book Synopsis Stochastic Processes: Theory and Methods by : D N Shanbhag

Download or read book Stochastic Processes: Theory and Methods written by D N Shanbhag and published by Gulf Professional Publishing. This book was released on 2001 with total page 990 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume in the series contains chapters on areas such as pareto processes, branching processes, inference in stochastic processes, Poisson approximation, Levy processes, and iterated random maps and some classes of Markov processes. Other chapters cover random walk and fluctuation theory, a semigroup representation and asymptomatic behavior of certain statistics of the Fisher-Wright-Moran coalescent, continuous-time ARMA processes, record sequence and their applications, stochastic networks with product form equilibrium, and stochastic processes in insurance and finance. Other subjects include renewal theory, stochastic processes in reliability, supports of stochastic processes of multiplicity one, Markov chains, diffusion processes, and Ito's stochastic calculus and its applications. c. Book News Inc.

Stochastic Processes: Modeling and Simulation

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Publisher : Gulf Professional Publishing
ISBN 13 : 9780444500137
Total Pages : 1028 pages
Book Rating : 4.5/5 (1 download)

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Book Synopsis Stochastic Processes: Modeling and Simulation by : D N Shanbhag

Download or read book Stochastic Processes: Modeling and Simulation written by D N Shanbhag and published by Gulf Professional Publishing. This book was released on 2003-02-24 with total page 1028 pages. Available in PDF, EPUB and Kindle. Book excerpt: This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme of reviewing and, in some cases, unifying with new ideas the different lines of research and developments in stochastic processes of applied flavour. This volume consists of 23 chapters addressing various topics in stochastic processes. These include, among others, those on manufacturing systems, random graphs, reliability, epidemic modelling, self-similar processes, empirical processes, time series models, extreme value therapy, applications of Markov chains, modelling with Monte Carlo techniques, and stochastic processes in subjects such as engineering, telecommunications, biology, astronomy and chemistry. particular with modelling, simulation techniques and numerical methods concerned with stochastic processes. The scope of the project involving this volume as well as volume 19 is already clarified in the preface of volume 19. The present volume completes the aim of the project and should serve as an aid to students, teachers, researchers and practitioners interested in applied stochastic processes.

Statistical Inference from Stochastic Processes

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Publisher : American Mathematical Soc.
ISBN 13 : 0821850873
Total Pages : 406 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Statistical Inference from Stochastic Processes by : Narahari Umanath Prabhu

Download or read book Statistical Inference from Stochastic Processes written by Narahari Umanath Prabhu and published by American Mathematical Soc.. This book was released on 1988 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Selected Works of C.C. Heyde

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Publisher : Springer Science & Business Media
ISBN 13 : 1441958231
Total Pages : 490 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Selected Works of C.C. Heyde by : Ross Maller

Download or read book Selected Works of C.C. Heyde written by Ross Maller and published by Springer Science & Business Media. This book was released on 2010-09-17 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 1945, very early in the history of the development of a rigorous analytical theory of probability, Feller (1945) wrote a paper called “The fundamental limit theorems in probability” in which he set out what he considered to be “the two most important limit theorems in the modern theory of probability: the central limit theorem and the recently discovered ... ‘Kolmogoroff’s cel ebrated law of the iterated logarithm’ ”. A little later in the article he added to these, via a charming description, the “little brother (of the central limit theo rem), the weak law of large numbers”, and also the strong law of large num bers, which he considers as a close relative of the law of the iterated logarithm. Feller might well have added to these also the beautiful and highly applicable results of renewal theory, which at the time he himself together with eminent colleagues were vigorously producing. Feller’s introductory remarks include the visionary: “The history of probability shows that our problems must be treated in their greatest generality: only in this way can we hope to discover the most natural tools and to open channels for new progress. This remark leads naturally to that characteristic of our theory which makes it attractive beyond its importance for various applications: a combination of an amazing generality with algebraic precision.

Essentials of Stochastic Processes

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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.

Martingale Methods in Statistics

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Publisher : CRC Press
ISBN 13 : 1466582820
Total Pages : 258 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Martingale Methods in Statistics by : Yoichi Nishiyama

Download or read book Martingale Methods in Statistics written by Yoichi Nishiyama and published by CRC Press. This book was released on 2021-11-24 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Martingale Methods in Statistics provides a unique introduction to statistics of stochastic processes written with the author’s strong desire to present what is not available in other textbooks. While the author chooses to omit the well-known proofs of some of fundamental theorems in martingale theory by making clear citations instead, the author does his best to describe some intuitive interpretations or concrete usages of such theorems. On the other hand, the exposition of relatively new theorems in asymptotic statistics is presented in a completely self-contained way. Some simple, easy-to-understand proofs of martingale central limit theorems are included. The potential readers include those who hope to build up mathematical bases to deal with high-frequency data in mathematical finance and those who hope to learn the theoretical background for Cox’s regression model in survival analysis. A highlight of the monograph is Chapters 8-10 dealing with Z-estimators and related topics, such as the asymptotic representation of Z-estimators, the theory of asymptotically optimal inference based on the LAN concept and the unified approach to the change point problems via "Z-process method". Some new inequalities for maxima of finitely many martingales are presented in the Appendix. Readers will find many tips for solving concrete problems in modern statistics of stochastic processes as well as in more fundamental models such as i.i.d. and Markov chain models.

Introduction to the Statistics of Poisson Processes and Applications

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Publisher : Springer Nature
ISBN 13 : 3031370546
Total Pages : 683 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Introduction to the Statistics of Poisson Processes and Applications by : Yury A. Kutoyants

Download or read book Introduction to the Statistics of Poisson Processes and Applications written by Yury A. Kutoyants and published by Springer Nature. This book was released on 2023-09-04 with total page 683 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers an extensive class of models involving inhomogeneous Poisson processes and deals with their identification, i.e. the solution of certain estimation or hypothesis testing problems based on the given dataset. These processes are mathematically easy-to-handle and appear in numerous disciplines, including astronomy, biology, ecology, geology, seismology, medicine, physics, statistical mechanics, economics, image processing, forestry, telecommunications, insurance and finance, reliability, queuing theory, wireless networks, and localisation of sources. Beginning with the definitions and properties of some fundamental notions (stochastic integral, likelihood ratio, limit theorems, etc.), the book goes on to analyse a wide class of estimators for regular and singular statistical models. Special attention is paid to problems of change-point type, and in particular cusp-type change-point models, then the focus turns to the asymptotically efficient nonparametric estimation of the mean function, the intensity function, and of some functionals. Traditional hypothesis testing, including some goodness-of-fit tests, is also discussed. The theory is then applied to three classes of problems: misspecification in regularity (MiR),corresponding to situations where the chosen change-point model and that of the real data have different regularity; optical communication with phase and frequency modulation of periodic intensity functions; and localization of a radioactive (Poisson) source on the plane using K detectors. Each chapter concludes with a series of problems, and state-of-the-art references are provided, making the book invaluable to researchers and students working in areas which actively use inhomogeneous Poisson processes.

Semiparametric Inference for Regression Models Based on Marked Point Processes

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Publisher : Herbert Utz Verlag
ISBN 13 : 9783896755902
Total Pages : 184 pages
Book Rating : 4.7/5 (559 download)

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Book Synopsis Semiparametric Inference for Regression Models Based on Marked Point Processes by : Alexander Luhm

Download or read book Semiparametric Inference for Regression Models Based on Marked Point Processes written by Alexander Luhm and published by Herbert Utz Verlag. This book was released on 1999 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Contemporary Developments in Statistical Theory

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Publisher : Springer Science & Business Media
ISBN 13 : 3319026518
Total Pages : 395 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Contemporary Developments in Statistical Theory by : Soumendra Lahiri

Download or read book Contemporary Developments in Statistical Theory written by Soumendra Lahiri and published by Springer Science & Business Media. This book was released on 2013-12-02 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume highlights Prof. Hira Koul’s achievements in many areas of Statistics, including Asymptotic theory of statistical inference, Robustness, Weighted empirical processes and their applications, Survival Analysis, Nonlinear time series and Econometrics, among others. Chapters are all original papers that explore the frontiers of these areas and will assist researchers and graduate students working in Statistics, Econometrics and related areas. Prof. Hira Koul was the first Ph.D. student of Prof. Peter Bickel. His distinguished career in Statistics includes the receipt of many prestigious awards, including the Senior Humbolt award (1995), and dedicated service to the profession through editorial work for journals and through leadership roles in professional societies, notably as the past president of the International Indian Statistical Association. Prof. Hira Koul has graduated close to 30 Ph.D. students, and made several seminal contributions in about 125 innovative research papers. The long list of his distinguished collaborators is represented by the contributors to this volume.

An Introduction to Stochastic Modeling

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Publisher : Academic Press
ISBN 13 : 1483269272
Total Pages : 410 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis An Introduction to Stochastic Modeling by : Howard M. Taylor

Download or read book An Introduction to Stochastic Modeling written by Howard M. Taylor and published by Academic Press. This book was released on 2014-05-10 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.