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 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.
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George G. Roussas received his B.A. in Mathematics at the University of Athens, Greece, and his Ph.D. in Statistics at the University of California, Berkeley. Roussas is currently Professor and Associate Dean of Statistics at the University of California, Davis. His teaching career began at the University of Wisconsin, Madison. Then he was a Professor of Applied Mathematics at the University of Patras, Greece, and also served as the Dean of the College of Sciences and as Chancellor of that University. At the University of Crete, Greece, Roussas served as Vice President of Academic Affairs. Roussas has published several books, and had more than 65 research papers published in refereed journals. He is a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, and the Royal Statistical Society, and is an elected member of the International Statistical Institute. Finally, Roussas is the Associate Editor of two journals, Statistics and Probability Letters, and Nonparametric Statistics.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Etat : New. 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 m. N° de réf. du vendeur 5891971
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Buch. Etat : Neu. Neuware - 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. N° de réf. du vendeur 9780128000427
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