Probability and Statistical Inference, 2nd Edition
Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstanding new edition continues to encouragereaders to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics. Clear explanations are presented and appliedto various examples that help to impart a deeper understanding of theorems and methods—from fundamental statistical concepts to computational details.
Additional features of this Second Edition include:
A new chapter on random samples
Coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book's FTP site
Treatment of survival and hazard function, methods of obtaining estimators, and Bayes estimating
Real-world examples that illuminate presented concepts
Exercises at the end of each section
Providing a straightforward, contemporary approach to modern-day statistical applications, Probability and Statistical Inference, Second Edition is an ideal text for advanced undergraduate- and graduate-level courses in probability and statistical inference. It also serves as a valuable reference for practitioners in any discipline who wish to gain further insight into the latest statistical tools.
1. Experiments, Sample Spaces, and Events.
4. Conditional Probability; Independence.
5. Markov Chains*.
6. Random Variables: Univariate Case.
7. Random Variables: Multivariate Case.
9. Selected Families of Distributions.
10. Random Samples.
11. Introduction to Statistical Inference.
13. Testing Statistical Hypotheses.
14. Linear Models.
15. Rank Methods.
16. Analysis of Categorical Data.
Answers to Odd-Numbered Problems.
The late Robert Bartoszynski, 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. Bartoszynski published over 80 journal articles, books, and book chapters. He was a Fellow of the Institute of Mathematical Statistics as well as a member of the American Statistical Association, the International Statistical Institute, and the Bernoulli Society.
Magdalena Niewiadomska-Bugaj, PhD, is Professor in the Department of Statistics at Western Michigan University. An active member of numerous societies including the American Statistical Association, the Institute of Mathematical Statistics, and the Classification Society of North America, 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.
- A new chapter on Random Samples and added new material on Monte Carlo Generations.
- Over 100 additional exercises (now graded by level of difficulty).
- Additional coverage of survival and hazard function, methods of obtaining estimators, Bayes and permutation testing.
- Several original, newly developed examples that illuminate the concepts.
- Comes packaged with an instructor's manual with solutions to every third problem in the text.
- Includes a wealth of examples illustrating concepts, theorems, and methods—from numerical data and details of calculations, to ideas behind some of the methods.
- Conveys accessible, user-friendly treatments that clearly explain concepts and motivations while pointing out pitfalls and difficulties of arguments.
- Incorporates a wide selection of advanced topics for students who would benefit from more thorough explanations.
- Provides coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book’s FTP site.
"The book is well written and contains many interesting examples and exercises. The emphasis in these and the exposition is clearly on mathematical development and theory." (Journal of the American Statistician, December 2008)
"...whether you are looking for a book for classroom adoption, or just want to brush up your basic probability skills by studying on your own, you will do yourself and your students a favor by considering this book." (MAA Review March 2008)