**Author**: Peter Olofsson,Mikael Andersson

**Publisher:**John Wiley & Sons

**ISBN:**0470889748

**Category:**Business & Economics

**Page:**558

**View:**6290

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# Search Results for: probability-statistics-and-stochastic-processes

**Author**: Peter Olofsson,Mikael Andersson

**Publisher:** John Wiley & Sons

**ISBN:** 0470889748

**Category:** Business & Economics

**Page:** 558

**View:** 6290

"This book provides a unique and balanced approach to probability, statistics, and stochastic processes. Readers gain a solid foundation in all three fields that serves as a stepping stone to more advanced investigations into each area. The Second Edition features new coverage of analysis of variance (ANOVA), consistency and efficiency of estimators, asymptotic theory for maximum likelihood estimators, empirical distribution function and the Kolmogorov-Smirnov test, general linear models, multiple comparisons, Markov chain Monte Carlo (MCMC), Brownian motion, martingales, and renewal theory. Many new introductory problems and exercises have also been added. This book combines a rigorous, calculus-based development of theory with a more intuitive approach that appeals to readers' sense of reason and logic, an approach developed through the author's many years of classroom experience. The book begins with three chapters that develop probability theory and introduce the axioms of probability, random variables, and joint distributions. The next two chapters introduce limit theorems and simulation. Also included is a chapter on statistical inference with a focus on Bayesian statistics, which is an important, though often neglected, topic for undergraduate-level texts. Markov chains in discrete and continuous time are also discussed within the book. More than 400 examples are interspersed throughout to help illustrate concepts and theory and to assist readers in developing an intuitive sense of the subject. Readers will find many of the examples to be both entertaining and thought provoking. This is also true for the carefully selected problems that appear at the end of each chapter"--

**Author**: Peter Olofsson,Mikael Andersson

**Publisher:** John Wiley & Sons

**ISBN:** 1118231309

**Category:** Mathematics

**Page:** 576

**View:** 4906

Praise for the First Edition ". . . an excellent textbook . . . well organized and neatlywritten." —Mathematical Reviews ". . . amazingly interesting . . ." —Technometrics Thoroughly updated to showcase the interrelationships betweenprobability, statistics, and stochastic processes, Probability,Statistics, and Stochastic Processes, Second Edition preparesreaders to collect, analyze, and characterize data in their chosenfields. Beginning with three chapters that develop probability theoryand introduce the axioms of probability, random variables, andjoint distributions, the book goes on to present limit theorems andsimulation. The authors combine a rigorous, calculus-baseddevelopment of theory with an intuitive approach that appeals toreaders' sense of reason and logic. Including more than 400examples that help illustrate concepts and theory, the SecondEdition features new material on statistical inference and a wealthof 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, SecondEdition is an excellent book for courses on probability andstatistics at the upper-undergraduate level. The book is also anideal resource for scientists and engineers in the fields ofstatistics, mathematics, industrial management, andengineering.
*Estimation and Prediction*

**Author**: D. Bosq

**Publisher:** Springer Science & Business Media

**ISBN:** 1461217180

**Category:** Mathematics

**Page:** 232

**View:** 2558

This book is devoted to the theory and applications of nonparametic functional estimation and prediction. Chapter 1 provides an overview of inequalities and limit theorems for strong mixing processes. Density and regression estimation in discrete time are studied in Chapter 2 and 3. The special rates of convergence which appear in continuous time are presented in Chapters 4 and 5. This second edition is extensively revised and it contains two new chapters. Chapter 6 discusses the surprising local time density estimator. Chapter 7 gives a detailed account of implementation of nonparametric method and practical examples in economics, finance and physics. Comarison with ARMA and ARCH methods shows the efficiency of nonparametric forecasting. The prerequisite is a knowledge of classical probability theory and statistics. Denis Bosq is Professor of Statistics at the Unviersity of Paris 6 (Pierre et Marie Curie). He is Editor-in-Chief of "Statistical Inference for Stochastic Processes" and an editor of "Journal of Nonparametric Statistics". He is an elected member of the International Statistical Institute. He has published about 90 papers or works in nonparametric statistics and four books.

**Author**: Denis Bosq

**Publisher:** John Wiley & Sons

**ISBN:** 1118586271

**Category:** Mathematics

**Page:** 304

**View:** 1578

Generally, books on mathematical statistics are restricted tothe case of independent identically distributed random variables.In this book however, both this case AND the case of dependentvariables, i.e. statistics for discrete and continuous timeprocesses, are studied. This second case is very important fortoday’s practitioners. Mathematical Statistics and Stochastic Processes is based ondecision theory and asymptotic statistics and contains up-to-dateinformation on the relevant topics of theory of probability,estimation, confidence intervals, non-parametric statistics androbustness, second-order processes in discrete and continuous timeand diffusion processes, statistics for discrete and continuoustime processes, statistical prediction, and complements inprobability. This book is aimed at students studying courses on probability withan emphasis on measure theory and for all practitioners who applyand use statistics and probability on a daily basis.

**Author**: N.U. Prabhu

**Publisher:** CRC Press

**ISBN:** 9780824784171

**Category:** Mathematics

**Page:** 288

**View:** 8929

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**: Kai L. Chung

**Publisher:** Springer-Verlag

**ISBN:** 3642670334

**Category:** Mathematics

**Page:** 346

**View:** 7456

Aus den Besprechungen: "Unter den zahlreichen Einführungen in die Wahrscheinlichkeitsrechnung bildet dieses Buch eine erfreuliche Ausnahme. Der Stil einer lebendigen Vorlesung ist über Niederschrift und Übersetzung hinweg erhalten geblieben. In jedes Kapitel wird sehr anschaulich eingeführt. Sinn und Nützlichkeit der mathematischen Formulierungen werden den Lesern nahegebracht. Die wichtigsten Zusammenhänge sind als mathematische Sätze klar formuliert." #FREQUENZ#1
*Statistics and Random Processes*

**Author**: Hossein Pishro-Nik

**Publisher:** N.A

**ISBN:** 9780990637202

**Category:** Probabilities

**Page:** 746

**View:** 7129

The book covers basic concepts such as random experiments, probability axioms, conditional probability, and counting methods, single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristic functions, random vectors, and inequalities; limit theorems and convergence; introduction to Bayesian and classical statistics; random processes including processing of random signals, Poisson processes, discrete-time and continuous-time Markov chains, and Brownian motion; simulation using MATLAB and R.

**Author**: Liliana Blanco Castañeda,Viswanathan Arunachalam,Selvamuthu Dharmaraja

**Publisher:** John Wiley & Sons

**ISBN:** 1118344960

**Category:** Mathematics

**Page:** 614

**View:** 7999

An easily accessible, real-world approach to probability andstochastic processes Introduction to Probability and Stochastic Processes withApplications presents a clear, easy-to-understand treatment ofprobability and stochastic processes, providing readers with asolid foundation they can build upon throughout their careers. Withan emphasis on applications in engineering, applied sciences,business and finance, statistics, mathematics, and operationsresearch, the book features numerous real-world examples thatillustrate how random phenomena occur in nature and how to useprobabilistic techniques to accurately model these phenomena. The authors discuss a broad range of topics, from the basicconcepts of probability to advanced topics for further study,including Itô integrals, martingales, and sigma algebras.Additional topical coverage includes: Distributions of discrete and continuous random variablesfrequently used in applications Random vectors, conditional probability, expectation, andmultivariate normal distributions The laws of large numbers, limit theorems, and convergence ofsequences of random variables Stochastic processes and related applications, particularly inqueueing systems Financial mathematics, including pricing methods such asrisk-neutral valuation and the Black-Scholes formula Extensive appendices containing a review of the requisitemathematics and tables of standard distributions for use inapplications are provided, and plentiful exercises, problems, andsolutions are found throughout. Also, a related website featuresadditional exercises with solutions and supplementary material forclassroom use. Introduction to Probability and StochasticProcesses with Applications is an ideal book for probabilitycourses at the upper-undergraduate level. The book is also avaluable reference for researchers and practitioners in the fieldsof engineering, operations research, and computer science whoconduct data analysis to make decisions in their everyday work.
*Probability and Mathematical Statistics: A Series of Monographs and Textbooks*

**Author**: Robert B. Ash,Melvin F. Gardner

**Publisher:** Academic Press

**ISBN:** 1483191435

**Category:** Mathematics

**Page:** 332

**View:** 3842

Topics in Stochastic Processes covers specific processes that have a definite physical interpretation and that explicit numerical results can be obtained. This book contains five chapters and begins with the L2 stochastic processes and the concept of prediction theory. The next chapter discusses the principles of ergodic theorem to real analysis, Markov chains, and information theory. Another chapter deals with the sample function behavior of continuous parameter processes. This chapter also explores the general properties of Martingales and Markov processes, as well as the one-dimensional Brownian motion. The aim of this chapter is to illustrate those concepts and constructions that are basic in any discussion of continuous parameter processes, and to provide insights to more advanced material on Markov processes and potential theory. The final chapter demonstrates the use of theory of continuous parameter processes to develop the Itô stochastic integral. This chapter also provides the solution of stochastic differential equations. This book will be of great value to mathematicians, engineers, and physicists.

**Author**: M. B. Rajarshi

**Publisher:** Springer Science & Business Media

**ISBN:** 8132207637

**Category:** Mathematics

**Page:** 113

**View:** 2409

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**: SHELDON M. ROSS

**Publisher:** N.A

**ISBN:** 9788126517572

**Category:**

**Page:** 532

**View:** 5658

Market_Desc: · Statisticians· Engineers· Computer Scientists· Senior/Graduate Level Students· Professors of Stochastics Processes Special Features: · Focuses on the application of stochastic process with emphasis on queuing networks and reversibility. · Describes processes from a probabilistic instead of an analytical point of view. About The Book: The book provides a non measure theoretic introduction to stochastic processes, probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments, Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs.

**Author**: Rabi N. Bhattacharya,Edward C. Waymire

**Publisher:** SIAM

**ISBN:** 0898716896

**Category:** Mathematics

**Page:** 184

**View:** 7762

This book develops systematically and rigorously, yet in an expository and lively manner, the evolution of general random processes and their large time properties such as transience, recurrence, and convergence to steady states. The emphasis is on the most important classes of these processes from the viewpoint of theory as well as applications, namely, Markov processes. The book features very broad coverage of the most applicable aspects of stochastic processes, including sufficient material for self-contained courses on random walks in one and multiple dimensions; Markov chains in discrete and continuous times, including birth-death processes; Brownian motion and diffusions; stochastic optimization; and stochastic differential equations. This book is for graduate students in mathematics, statistics, science and engineering, and it may also be used as a reference by professionals in diverse fields whose work involves the application of probability.
*In Honour of Ole E. Barndorff-Nielsen*

**Author**: Mark Podolskij,Robert Stelzer,Steen Thorbjørnsen,Almut E. D. Veraart

**Publisher:** Springer

**ISBN:** 3319258265

**Category:** Mathematics

**Page:** 527

**View:** 891

Collecting together twenty-three self-contained articles, this volume presents the current research of a number of renowned scientists in both probability theory and statistics as well as their various applications in economics, finance, the physics of wind-blown sand, queueing systems, risk assessment, turbulence and other areas. The contributions are dedicated to and inspired by the research of Ole E. Barndorff-Nielsen who, since the early 1960s, has been and continues to be a very active and influential researcher working on a wide range of important problems. The topics covered include, but are not limited to, econometrics, exponential families, Lévy processes and infinitely divisible distributions, limit theory, mathematical finance, random matrices, risk assessment, statistical inference for stochastic processes, stochastic analysis and optimal control, time series, and turbulence. The book will be of interest to researchers and graduate students in probability, statistics and their applications.
*Stochastic Models and Statistical Inference*

**Author**: Denis Bosq,Hung T. Nguyen

**Publisher:** Springer Science & Business Media

**ISBN:** 9401587698

**Category:** Mathematics

**Page:** 354

**View:** 3150

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**: Jan Vrbik,Paul Vrbik

**Publisher:** Springer Science & Business Media

**ISBN:** 1461440572

**Category:** Mathematics

**Page:** 287

**View:** 2988

The book presents an introduction to Stochastic Processes including Markov Chains, Birth and Death processes, Brownian motion and Autoregressive models. The emphasis is on simplifying both the underlying mathematics and the conceptual understanding of random processes. In particular, non-trivial computations are delegated to a computer-algebra system, specifically Maple (although other systems can be easily substituted). Moreover, great care is taken to properly introduce the required mathematical tools (such as difference equations and generating functions) so that even students with only a basic mathematical background will find the book self-contained. Many detailed examples are given throughout the text to facilitate and reinforce learning. Jan Vrbik has been a Professor of Mathematics and Statistics at Brock University in St Catharines, Ontario, Canada, since 1982. Paul Vrbik is currently a PhD candidate in Computer Science at the University of Western Ontario in London, Ontario, Canada. .

**Author**: Sidney I. Resnick

**Publisher:** Springer Science & Business Media

**ISBN:** 1461203872

**Category:** Mathematics

**Page:** 626

**View:** 4416

Stochastic processes are necessary ingredients for building models of a wide variety of phenomena exhibiting time varying randomness. This text offers easy access to this fundamental topic for many students of applied sciences at many levels. It includes examples, exercises, applications, and computational procedures. It is uniquely useful for beginners and non-beginners in the field. No knowledge of measure theory is presumed.
*In Engineering and Physical Sciences*

**Author**: Michel K. Ochi

**Publisher:** Wiley-Interscience

**ISBN:** N.A

**Category:** Mathematics

**Page:** 520

**View:** 8936

This introduction to modern concepts of applied stochastic processes is written for a broad range of applications in diverse areas of engineering and the physical sciences (unlike other books, which are written primarily for communications or electrical engineering). Emphasis is on clarifying the basic principles supporting current prediction techniques. The first eight chapters present the probability theory relevant to analysis of stochastic processes. The following nine chapters discuss principles, advanced techniques (including the procedures of spectral analysis and the development of the probability density function) and applications. Also features material found in the recent literature such as higher-order spectral analysis, the joint probability distribution of amplitudes and periods and non-Gaussian random processes. Includes numerous illustrative examples.

**Author**: J. K. Lindsey

**Publisher:** Cambridge University Press

**ISBN:** 9781139454513

**Category:** Mathematics

**Page:** 338

**View:** 3482

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.

**Author**: Y. Rozanov

**Publisher:** Springer Science & Business Media

**ISBN:** 9401104492

**Category:** Mathematics

**Page:** 259

**View:** 8898

Probability Theory, Theory of Random Processes and Mathematical Statistics are important areas of modern mathematics and its applications. They develop rigorous models for a proper treatment for various 'random' phenomena which we encounter in the real world. They provide us with numerous tools for an analysis, prediction and, ultimately, control of random phenomena. Statistics itself helps with choice of a proper mathematical model (e.g., by estimation of unknown parameters) on the basis of statistical data collected by observations. This volume is intended to be a concise textbook for a graduate level course, with carefully selected topics representing the most important areas of modern Probability, Random Processes and Statistics. The first part (Ch. 1-3) can serve as a self-contained, elementary introduction to Probability, Random Processes and Statistics. It contains a number of relatively sim ple and typical examples of random phenomena which allow a natural introduction of general structures and methods. Only knowledge of elements of real/complex analysis, linear algebra and ordinary differential equations is required here. The second part (Ch. 4-6) provides a foundation of Stochastic Analysis, gives information on basic models of random processes and tools to study them. Here a familiarity with elements of functional analysis is necessary. Our intention to make this course fast-moving made it necessary to present important material in a form of examples.

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