Peter Olofsson, Mikael Andersson

Probability, Statistics, and Stochastic Processes

• Gebundenes Buch

Produktbeschreibung

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.

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Praise for the First Edition

". . . an excellent textbook . . . well organized and neatly written."
--Mathematical Reviews

". . . amazingly interesting . . ."
--Technometrics

Thoroughly updated to showcase the interrelationships between probability, statistics, and stochastic processes, Probability, Statistics, and Stochastic Processes, Second Edition prepares readers to collect, analyze, and characterize data in their chosen fields.

Beginning with three chapters that develop probability theory and introduce the axioms of probability, random variables, and joint distributions, the book goes on to present limit theorems and simulation. The authors combine a rigorous, calculus-based development of theory with an intuitive approach that appeals to readers' sense of reason and logic. Including more than 400 examples that help illustrate concepts and theory, the Second Edition features new material on statistical inference and a wealth of 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, Second Edition is an excellent book for courses on probability and statistics at the upper-undergraduate level. The book is also an ideal resource for scientists and engineers in the fields of statistics, mathematics, industrial management, and engineering.

Produktdetails

• Verlag: Wiley & Sons
• 2. Aufl.
• Seitenzahl: 576
• Erscheinungstermin: 22. Mai 2012
• Englisch
• Abmessung: 240mm x 161mm x 35mm
• Gewicht: 960g
• ISBN-13: 9780470889749
• ISBN-10: 0470889748
• Artikelnr.: 35046601

Autorenporträt

PETER OLOFSSON, PhD, is Professor in the Mathematics Department at Trinity University. Dr. Olofsson's research interests include stochastic processes, branching processes, mathematical biology, and Poisson approximation. He is the author of Probabilities: The Little Numbers That Rule Our Lives, also published by Wiley. MIKAEL ANDERSSON, PhD, is Associate Professor in the Department of Applied Statistics at the Swedish University of Agricultural Sciences. Dr. Andersson's research interests include stochastic modeling of infectious diseases, epidemiology, and biological applications.

Inhaltsangabe

Preface xi Preface to the First Edition xiii 1 Basic Probability Theory 1
1.1 Introduction 1 1.2 Sample Spaces and Events 3 1.3 The Axioms of
Probability 7 1.4 Finite Sample Spaces and Combinatorics 15 1.4.1
Combinatorics 17 1.5 Conditional Probability and Independence 27 1.6 The
Law of Total Probability and Bayes' Formula 41 Problems 63 2 Random
Variables 76 2.1 Introduction 76 2.2 Discrete Random Variables 77 2.3
Continuous Random Variables 82 2.4 Expected Value and Variance 95 2.5
Special Discrete Distributions 111 2.6 The Exponential Distribution 123 2.7
The Normal Distribution 127 2.8 Other Distributions 131 2.9 Location
Parameters 137 2.10 The Failure Rate Function 139 Problems 144 3 Joint
Distributions 156 3.1 Introduction 156 3.2 The Joint Distribution Function
156 3.3 Discrete Random Vectors 158 3.4 Jointly Continuous Random Vectors
160 3.5 Conditional Distributions and Independence 164 3.5.1 Independent
Random Variables 168 3.6 Functions of Random Vectors 172 3.7 Conditional
Expectation 185 3.8 Covariance and Correlation 196 3.9 The Bivariate Normal
Distribution 209 3.10 Multidimensional Random Vectors 216 3.11 Generating
Functions 231 3.12 The Poisson Process 240 Problems 247 4 Limit Theorems
263 4.1 Introduction 263 4.2 The Law of Large Numbers 264 4.3 The Central
Limit Theorem 268 4.4 Convergence in Distribution 275 Problems 278 5
Simulation 281 5.1 Introduction 281 5.2 Random Number Generation 282 5.3
Simulation of Discrete Distributions 283 5.4 Simulation of Continuous
Distributions 285 5.5 Miscellaneous 290 Problems 292 6 Statistical
Inference 294 6.1 Introduction 294 6.2 Point Estimators 294 6.3 Confidence
Intervals 304 6.4 Estimation Methods 312 6.5 Hypothesis Testing 327 6.6
Further Topics in Hypothesis Testing 334 6.7 Goodness of Fit 339 6.8
Bayesian Statistics 351 6.9 Nonparametric Methods 363 Problems 378 7 Linear
Models 391 7.1 Introduction 391 7.2 Sampling Distributions 392 7.3 Single
Sample Inference 395 7.4 Comparing Two Samples 402 7.5 Analysis of Variance
409 7.6 Linear Regression 415 7.7 The General Linear Model 431 Problems 436
8 Stochastic Processes 444 8.1 Introduction 444 8.2 Discrete -Time Markov
Chains 445 8.3 Random Walks and Branching Processes 464 8.4 Continuous
-Time Markov Chains 475 8.5 Martingales 494 8.6 Renewal Processes 502 8.7
Brownian Motion 509 Problems 517 Appendix A Tables 527 Appendix B Answers
to Selected Problems 535 Further Reading 551 Index 553