An Introduction to Measure-Theoretic Probability, Second Edition, employs a classical approach to teaching the basics of measure theoretic probability. This book provides in a concise, yet detailed way, the bulk of the probabilistic tools that a student working toward an advanced degree in statistics, probability and other related areas should be equipped with.
This edition requires no prior knowledge of measure theory, covers all its topics in great detail, and includes one chapter on the basics of ergodic theory and one chapter on two cases of statistical estimation. Topics range from the basic properties of a measure to modes of convergence of a sequence of random variables and their relationships; the integral of a random variable and its basic properties; standard convergence theorems; standard moment and probability inequalities; the Hahn-Jordan Decomposition Theorem; the Lebesgue Decomposition T; conditional expectation and conditional probability; theory of characteristic functions; sequences of independent random variables; and ergodic theory. There is a considerable bend toward the way probability is actually used in statistical research, finance, and other academic and nonacademic applied pursuits. Extensive exercises and practical examples are included, and all proofs are presented in full detail. Complete and detailed solutions to all exercises are available to the instructors on the book companion site.
This text will be a valuable resource for graduate students primarily in statistics, mathematics, electrical and computer engineering or other information sciences, as well as for those in mathematical economics/finance in the departments of economics.
Considerable bend toward the way probability is used in statistics in non-mathematical settings in academic, research and corporate/finance pursuits.
"synopsis" may belong to another edition of this title.
An Introduction to Measure-Theoretic Probability, Second Edition, employs a classical approach to teaching students of statistics, mathematics, engineering, econometrics, finance, and other disciplines that measure theoretic probability. This book requires no prior knowledge of measure theory, discusses all its topics in great detail, and includes one chapter on the basics of ergodic theory and one chapter on two cases of statistical estimation. There is a considerable bend toward the way probability is actually used in statistical research, finance, and other academic and nonacademic applied pursuits. This book provides in a concise, yet detailed way, the bulk of the probabilistic tools that a student working toward an advanced degree in statistics, probability and other related areas should be equipped with.
Considerable bend toward the way probability is used in statistics in non-mathematical settings in academic, research and corporate/finance pursuits.
An Introduction to Measure-theoretic Probability, Second Edition, employs a classical approach to teaching students of statistics, mathematics, engineering, econometrics, finance, and other disciplines measure-theoretic probability. This book requires no prior knowledge of measure theory, discusses all its topics in great detail, and includes information on the basics of ergodic theory and cases of statistical estimation. There is a considerable bend toward the way probability is actually used in statistical research, finance, and other academic and nonacademic applied pursuits.
Key Features
. Provides in a concise, yet detailed way, the bulk of probabilistic tools essential to a student working toward an advanced degree in statistics, probability, and other related fields
. Includes extensive exercises and practical examples to make complex ideas of advanced probability accessible to graduate students in statistics, probability, and related fields
. All proofs presented in full detail and complete and detailed solutions to all exercises are available to the instructors on book companion site
About the Author
George G. Roussas is a Distinguished Professor Emeritus (as of July 01, 2012) of Statistics at the University of California, Davis (UC-Davis), and a well-known author of books, research monographs, editor/co-editor of special volumes, and author/co-author of dozens of research articles published in leading journals of the profession. He is a Fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the Royal Statistical Society (RSS), an Elected Member of the International Statistical Institute (ISI), and a Corresponding Member of the Academy of Athens. Roussas was an associate editor of four journals since their inception, and is now a member of the Editorial Board of the journal Statistical Inference for Stochastic Processes. He served as an Associate Dean, Dean, Vice President, and Chancellor at three universities, and he has been instrumental in rendering statistics at UC-Davis nationally and internationally renown. Roussas has been honored with a Festschrift, and he has given interviews for the Statistical Science and the Statistical Periscope. He has contributed an obituary to the Bulletin of IMS for Professor -Academician David Blackwell of the UC-Berkeley, and he has been the co-ordinating editor of an article of contributions for same, which appeared in the Notices of the American Mathematical Society; it will be republished in Celebratio Matheimatica.
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