An Introduction to Probability and Statistical Inference

An Introduction to Probability and Statistical Inference

by George G. Roussas
An Introduction to Probability and Statistical Inference

An Introduction to Probability and Statistical Inference

by George G. Roussas

eBook

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Overview

Roussas introduces readers with no prior knowledge in probability or statistics, to a thinking process to guide them toward the best solution to a posed question or situation. An Introduction to Probability and Statistical Inference provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. "The text is wonderfully written and has the mostcomprehensive range of exercise problems that I have ever seen." — Tapas K. Das, University of South Florida"The exposition is great; a mixture between conversational tones and formal mathematics; the appropriate combination for a math text at [this] level. In my examination I could find no instance where I could improve the book." — H. Pat Goeters, Auburn, University, Alabama

* Contains more than 200 illustrative examples discussed in detail, plus scores of numerical examples and applications* Chapters 1-8 can be used independently for an introductory course in probability* Provides a substantial number of proofs

Product Details

ISBN-13: 9780080495750
Publisher: Elsevier Science
Publication date: 02/13/2003
Sold by: Barnes & Noble
Format: eBook
Pages: 523
File size: 20 MB
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About the Author

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.

Table of Contents

Chapter 1 Some Motivating Examples and Some
Fundamental Concepts; Chapter 2 The Concept of Probability and Some Basic Results; Chapter 3 Numerical Characteristics of a Random Variable, Some Special Random Variables; Chapter 4 Joint and Conditionalp.d.f:s, Conditional Expectation and Variance, Moment Generating Function, Covariance and Correlation Coefficient; Chapter 5 Independence of Random Variables and Some Applications; Chapter 6 Transformation of Random Variables; Chapter 7 Some Modes of Convergence of Random Variables, Applications; Chapter 8 An Overview of Statistical Inference; Chapter 9 Point Estimation; Chapter 10 Confidence Intervals and Confidence Regions; Chapter 11 Testing Hypotheses; Chapter 12 More About Testing Hypotheses; Chapter 13 A Simple Linear Regression Model; Chapter 14 Two Analysis of Variance Models; Chapter 15 Some Topics in Nonparametric Inference

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Valuable resource for advanced students and users to encourage critical thinking and application of key methods

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