Now updated in a valuable new edition–this user-friendly book
focuses on understanding the ‘why’ of mathematical statistics
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.
Tabla de materias
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.
Statistical Tables.
Bibliography.
Answers to Odd-Numbered Problems.
Index.
Sobre el autor
The late Robert Bartoszynski, Ph D, 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, Ph D, 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.
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