Magdalena Niewiadomska-Bugaj, Robert Bartoszynski

Probability and Statistical Inference (eBook, PDF)

• Format: PDF

Produktbeschreibung

Updated classic statistics text, with new problems and examples Probability and Statistical Inference, Third Edition helps students grasp essential concepts of statistics and its probabilistic foundations. This book focuses on the development of intuition and understanding in the subject through a wealth of examples illustrating concepts, theorems, and methods. The reader will recognize and fully understand the why and not just the how behind the introduced material. In this Third Edition, the reader will find a new chapter on Bayesian statistics, 70 new problems and an appendix with the supporting R code. This book is suitable for upper-level undergraduates or first-year graduate students studying statistics or related disciplines, such as mathematics or engineering. This Third Edition: * Introduces an all-new chapter on Bayesian statistics and offers thorough explanations of advanced statistics and probability topics * Includes 650 problems and over 400 examples - an excellent resource for the mathematical statistics class sequence in the increasingly popular "flipped classroom" format * Offers students in statistics, mathematics, engineering and related fields a user-friendly resource * Provides practicing professionals valuable insight into statistical tools Probability and Statistical Inference offers a unique approach to problems that allows the reader to fully integrate the knowledge gained from the text, thus, enhancing a more complete and honest understanding of the topic.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 592
• Erscheinungstermin: 23. November 2020
• Englisch
• ISBN-13: 9781119243816
• Artikelnr.: 60708871

Autorenporträt

MAGDALENA NIEWIADOMSKA-BUGAJ, PHD, is Professor and Chair of the Statistics Department at Western Michigan University. Dr. Niewiadomska-Bugaj's areas of interest include general statistical methodology, nonparametric statistics, classification, and categorical data analysis. She has published over 50 papers, books, and book chapters in theoretical and applied statistics The late ROBERT BARTOSZY?SKI, PHD, was Professor in the Department of Statistics at The Ohio State University. His scientific contributions included research in the theory of stochastic processes and modeling biological phenomena. Throughout his career, Dr Bartoszy?ski published books, book chapters, and over 100 journal articles.

Inhaltsangabe

Preface to Third Edition xi

Preface to Second Edition xiii

About the Companion Website xvi

1 Experiments, Sample Spaces, and Events 1

1.1 Introduction 1

1.2 Sample Space 2

1.3 Algebra of Events 8

1.4 Infinite Operations on Events 13

2 Probability 21

2.1 Introduction 21

2.2 Probability as a Frequency 21

2.3 Axioms of Probability 22

2.4 Consequences of the Axioms 26

2.5 Classical Probability 30

2.6 Necessity of the Axioms 31

2.7 Subjective Probability 35

3 Counting 39

3.1 Introduction 39

3.2 Product Sets, Orderings, and Permutations 39

3.3 Binomial Coefficients 44

3.4 Multinomial Coefficients 56

4 Conditional Probability, Independence, and Markov Chains 59

4.1 Introduction 59

4.2 Conditional Probability 60

4.3 Partitions; Total Probability Formula 65

4.4 Bayes' Formula 69

4.5 Independence 74

4.6 Exchangeability; Conditional Independence 80

4.7 Markov Chains* 82

5 Random Variables: Univariate Case 93

5.1 Introduction 93

5.2 Distributions of Random Variables 94

5.3 Discrete and Continuous Random Variables 102

5.4 Functions of Random Variables 112

5.5 Survival and Hazard Functions 118

6 Random Variables: Multivariate Case 123

6.1 Bivariate Distributions 123

6.2 Marginal Distributions; Independence 129

6.3 Conditional Distributions 140

6.4 Bivariate Transformations 147

6.5 Multidimensional Distributions 155

7 Expectation 163

7.1 Introduction 163

7.2 Expected Value 164

7.3 Expectation as an Integral 171

7.4 Properties of Expectation 177

7.5 Moments 184

7.6 Variance 191

7.7 Conditional Expectation 202

7.8 Inequalities 206

8 Selected Families of Distributions 211

8.1 Bernoulli Trials and Related Distributions 211

8.2 Hypergeometric Distribution 223

8.3 Poisson Distribution and Poisson Process 228

8.4 Exponential, Gamma, and Related Distributions 240

8.5 Normal Distribution 246

8.6 Beta Distribution 255

9 Random Samples 259

9.1 Statistics and Sampling Distributions 259

9.2 Distributions Related to Normal 261

9.3 Order Statistics 266

9.4 Generating Random Samples 272

9.5 Convergence 276

9.6 Central Limit Theorem 287

10 Introduction to Statistical Inference 295

10.1 Overview 295

10.2 Basic Models 298

10.3 Sampling 299

10.4 Measurement Scales 305

11 Estimation 309

11.1 Introduction 309

11.2 Consistency 313

11.3 Loss, Risk, and Admissibility 316

11.4 Efficiency 321

11.5 Methods of Obtaining Estimators 328

11.6 Sufficiency 345

11.7 Interval Estimation 359

12 Testing Statistical Hypotheses 373

12.1 Introduction 373

12.2 Intuitive Background 377

12.3 Most Powerful Tests 384

12.4 Uniformly Most Powerful Tests 396

12.5 Unbiased Tests 402

12.6 Generalized Likelihood Ratio Tests 405

12.7 Conditional Tests 412

12.8 Tests and Confidence Intervals 415

12.9 Review of Tests for Normal Distributions 416

12.10 Monte Carlo, Bootstrap, and Permutation Tests 424

13 Linear Models 429

13.1 Introduction 429

13.2