Introduction to Measure-Theoretic Probability (eBook)

George G. Roussas (Autor)

eBook Download: PDF | EPUB

2014 | 2. Auflage
426 Seiten
Elsevier Science (Verlag)
978-0-12-800290-2 (ISBN)

Infos

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. - 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 - Considerable bend toward the way probability is used in statistics in non-mathematical settings in academic, research and corporate/finance pursuits

George G. Roussas earned a B.S. in Mathematics with honors from the University of Athens, Greece, and a Ph.D. in Statistics from the University of California, Berkeley. As of July 2014, he is a Distinguished Professor Emeritus of Statistics at the University of California, Davis. Roussas is the author of five books, the author or co-author of five special volumes, and the author or co-author of dozens of research articles published in leading journals and special volumes. He is a Fellow of the following professional societies: The American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), The Royal Statistical Society (RSS), the American Association for the Advancement of Science (AAAS), and an Elected Member of the International Statistical Institute (ISI); also, he is 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. Throughout his career, Roussas served as Dean, Vice President for Academic Affairs, and Chancellor at two universities; also, he served as an Associate Dean at UC-Davis, helping to transform that institution's statistical unit into one of national and international renown. Roussas has been honored with a Festschrift, and he has given featured interviews for the Statistical Science and the Statistical Periscope. He has contributed an obituary to the IMS Bulletin for Professor-Academician David Blackwell of UC-Berkeley, and has been the coordinating editor of an extensive article of contributions for Professor Blackwell, which was published in the Notices of the American Mathematical Society and the Celebratio Mathematica.

Textauszug

Preface to First Edition

This book in measure-theoretic probability has resulted from classroom lecture notes that this author has developed over a number of years, by teaching such a course at both the University of Wisconsin, Madison, and the University of California, Davis. The audience consisted of graduate students primarily in statistics and mathematics. There were always some students from engineering departments, and a handful of students from other disciplines such as economics.

The book is not a comprehensive treatment of probability, nor is it meant to be one. Rather, it is an excursion in measure-theoretic probability with the objective of introducing the student to the basic tools in measure theory and probability as they are commonly used in statistics, mathematics, and other areas employing this kind of moderately advanced mathematical machinery. Furthermore, it must be emphasized that the approach adopted here is entirely classical. Thus, characteristic functions are a tool employed extensively; no use of martingale or empirical process techniques is made anywhere.

The book does not commence with probabilistic concepts, and there is a good reason for it. As many of those engaged in teaching advanced probability and statistical theory know, very few students, if any, have been exposed to a measure theory course prior to attempting a course in advanced probability. This has been invariably the experience of this author throughout the years. This very fact necessitates the study of the basic measure-theoretic concepts and results—in particular, the study of those concepts and results that apply immediately to probability, and also in the form and shape they are used in probability.

On the basis of such considerations, the framework of the material to be dealt with is therefore determined. It consists of a brief introduction to measure theory, and then the discussion of those probability results that constitute the backbone of the subject matter. There is minimal flexibility allowed, and that is exploited in the form of the final chapter of the book. From many interesting and important candidate topics, this author has chosen to present a brief discussion of some basic concepts and results of ergodic theory.

From the very outset, there is one point that must be abundantly clarified, and that is the fact that everything is discussed in great detail with all proofs included; no room is allowed for summary unproven statements. This approach has at least two side benefits, as this author sees them. One is that students have at their disposal a comprehensive and detailed proof of what are often deep theorems. Second, the instructor may skip the reproduction of such proofs by assigning their study to students.

In the experience of this author, there are no topics in this book which can be omitted, except perhaps for the final chapter. With this in mind, the material can be taught in two quarters, and perhaps even in one semester with appropriate calibration of the rate of presentation, and the omission of proofs of judiciously selected theorems. With all details presented, one can also cover an entire year of instruction, perhaps with some supplementation.

Most chapters are supplied with examples, and all chapters are concluded with a varying number of exercises. An unusual feature here is that an Answers Manual of all exercises will be made available to those instructors who adopt the book as the textbook in their course. Furthermore, an overview of each one of the 15 chapters is included in an appendix to the main body of the book. It is believed that the reader will benefit significantly by reviewing the overview of a chapter before the material in the chapter itself is discussed.

The remainder of this preface is devoted to a brief presentation of the material discussed in the 15 chapters of the book, chapter-by-chapter.

Chapter 1 commences with the introduction of the important classes of sets in an abstract space, which are those of a field, a σ-field, including the Borel σ-field, and a monotone class. They are illustrated by concrete examples, and their relationships are studied. Product spaces are also introduced, and some basic results are established. The discussion proceeds with the introduction of the concept of measurable functions, and in particular of random vectors and random variables. Some related results are also presented. This chapter is concluded with a fundamental theorem, Theorem 17, which provides for pointwise approximation of any random variable by a sequence of so-called simple random variables.

Chapter 2 is devoted to the introduction of the concept of a measure, and the study of the most basic results associated with it. Although a field is the class over which a measure can be defined in an intuitively satisfying manner, it is a σ-field—the one generated by an underlying field—on which a measure must be defined. One way of carrying out the construction of a measure on a σ-field is to use as a tool the so-called outer measure. The concept of an outer measure is then introduced, and some of its properties are studied in the second section of the chapter. Thus, starting with a measure on a field, utilizing the associated outer measure and the powerful Carathéodory extension theorem, one ensures the definition of a measure over the σ-field generated by the underlying field. The chapter is concluded with a study of the relationship between a measure over the Borel σ-field in the real line and certain point functions. A measure always determines a class of point functions, which are nondecreasing and right-continuous. The important thing, however, is that each such point function uniquely determines a measure on the Borel σ-field.

In Chapter 3, sequences of random variables are considered, and two basic kinds of convergences are introduced. One of them is the almost everywhere convergence, and the other is convergence in measure. The former convergence is essentially the familiar pointwise convergence, whereas convergence in measure is a mode of convergence not occurring in a calculus course. A precise expression of the set of pointwise convergence is established, which is used for formulating necessary and sufficient conditions for almost everywhere convergence. Convergence in measure is weaker than almost everywhere convergence, and the latter implies the former for finite measures. Almost everywhere convergence and mutual almost everywhere convergence are equivalent, as is easily seen. Although the same is true when convergence in measure is involved, its justification is fairly complicated and requires the introduction of the concept of almost uniform convergence. Actually, a substantial part of the chapter is devoted in proving the equivalence just stated. In closing, it is to be mentioned that, in the presence of a probability measure, almost everywhere convergence and convergence in measure become, respectively, almost sure convergence and convergence in probability.

Chapter 4 is devoted to the introduction of the concept of the integral of a random variable with respect to a measure, and the proof of some fundamental properties of the integral. When the underlying measure is a probability measure, the integral of a random variable becomes its expectation. The procedure of defining the concept of the integral follows three steps. The integral is first defined for a simple random variable, then for a nonnegative random variable, and finally for any random variable, provided the last step produces a meaningful quantity. This chapter is concluded with a result, Theorem 13, which transforms integration of a function of a random variable on an abstract probability space into integration of a real-valued function defined on the real line with respect to a probability measure on the Borel σ-field, which is the probability distribution of the random variable involved.

Chapter 5 is the first chapter where much of what was derived in the previous chapters is put to work. This chapter provides results that in a real sense constitute the workhorse whenever convergence of integrals is concerned, or differentiability under an integral sign is called for, or interchange of the order of integration is required. Some of the relevant theorems here are known by names such as the Lebesgue Monotone Convergence Theorem, the Fatou–Lebesgue Theorem, the Dominated Convergence Theorem, and the Fubini Theorem. Suitable modifications of the basic theorems in the chapter cover many important cases of both theoretical and applied interest. This is also the appropriate point to mention that many properties involving integrals are established by following a standard methodology; namely, the property in question is first proved for indicator functions, then for nonnegative simple random variables, next for nonnegative random variables, and finally for any random variables. Each step in this process relies heavily on the previous step, and the Lebesgue Monotone Convergence Theorem plays a central role.

Chapter 6 is the next chapter in which results of great utilitarian value are established. These results include the standard inequalities (Hölder (Cauchy–Schwarz), Minkowski, cr, Jensen), and a combination of a probability/moment inequality, which produces the Markov and Tchebichev inequalities. A third kind of convergence—convergence in the rth mean—is also introduced and studied to a considerable extent. It is shown that convergence in the rth mean...

Produkt-Details

• Erscheint lt. Verlag: 19.3.2014
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• ISBN-10: 0-12-800290-5 / 0128002905
• ISBN-13: 978-0-12-800290-2 / 9780128002902