Description
Book SynopsisPraise 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 stati
Table of Contents
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
