Probability and Statistical Inference 2/e (絕)
- 20本以上，享 8.5折
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- 一般書籍
- ISBN：9780471696933
- 作者：Robert Bartoszynski, Magdalena Niewiadomska-Bugaj
- 版次：2
- 年份：2008
- 出版商：John Wiley
書籍介紹
本書特色
目錄
作者介紹
Description
Probability and Statistical Inference, Second Edition introduces key probability and statistical concepts through non-trivial, real-world examples and promotes the development of intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comprehensive tools needed to develop a broad understanding of the theory of statistics and its probabilistic foundations. This outstanding new edition continues to encourage readers to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics.
Probability and Statistical Inference, Second Edition introduces key probability and statistical concepts through non-trivial, real-world examples and promotes the development of intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comprehensive tools needed to develop a broad understanding of the theory of statistics and its probabilistic foundations. This outstanding new edition continues to encourage readers to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics.
Features
New to This Edition
- 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.
New to This Edition
- 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.
Table of Contents
Preface
1. Experiments, Sample Spaces, and Events
2. Probability
3. Counting
4. Conditional Probability; Independence
5. Markov Chains*
6. Random Variables: Univariate Case
7. Random Variables: Multivariate Case
8. Expectation
9. Selected Families of Distributions
10. Random Samples
11. Introduction to Statistical Inference
12. Estimation
13. Testing Statistical Hypotheses
14. Linear Models
15. Rank Methods
16. Analysis of Categorical Data
Index
Preface
1. Experiments, Sample Spaces, and Events
2. Probability
3. Counting
4. Conditional Probability; Independence
5. Markov Chains*
6. Random Variables: Univariate Case
7. Random Variables: Multivariate Case
8. Expectation
9. Selected Families of Distributions
10. Random Samples
11. Introduction to Statistical Inference
12. Estimation
13. Testing Statistical Hypotheses
14. Linear Models
15. Rank Methods
16. Analysis of Categorical Data
Index
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.
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.
