Description

For courses in mathematical statistics.

Comprehensive coverage of mathematical statistics – with a proven approach

Introduction to Mathematical Statistics by Hogg, McKean, and Craig enhances student comprehension and retention with numerous, illustrative examples and exercises. Classical statistical inference procedures in estimation and testing are explored extensively, and the text’s flexible organization makes it ideal for a range of mathematical statistics courses.

Substantial changes to the 8th Edition – many based on user feedback – help students appreciate the connection between statistical theory and statistical practice, while other changes enhance the development and discussion of the statistical theory presented.

Features

A comprehensive introduction to mathematical statistics with a proven approach

· In-depth treatment of sufficiency and testing theory includes uniformly most powerful tests and likelihood ratio tests.

· The text’s flexible organization makes it ideal for use with a range of mathematical statistics courses.

· New - Many additional real data sets to illustrate statistical methods or compare methods.

o The data sets are also available to students in the free R package hmcpkg. They can also be individually downloaded in an R session at https://media.pearsoncmg.com/intl/ge/abp/resources/products/product.html#product,isbn=9781292264769

o The R code for the analyses on these data sets are generally given in the text for the students’ benefit.

· Revised - Expanded use of the statistical software R, a powerful statistical language which is free and can run on all three main platforms. However, instructors can choose another statistical package if desired.

o Use of R functions is increased to compute analyses and simulation studies, including several games.

o A brief R primer in Appendix B suffices for the understanding of the R used in the text.

· New - Downloadable, supplemented mathematical review material in Appendix A: reviews sequences, infinite series, differentiation, and integration (univariate and bivariate).

· Revised - Expanded discussion of iterated integrals, with added figures to clarify discussion.

· New - A new subsection on the bivariate normal distribution begins the section on the multivariate normal distribution in Chapter 3 (Some Special Distributions).

· New - Several important topics have been added, including Tukey’s multiple comparison procedure in Chapter 9 (Inferences About Normal Linear Models)and confidence intervals for the correlation coefficients found in Chapters 9 and 10 (Nonparametric and Robust Statistics).

· New - Discussion on standard errors for estimates obtained by bootstrapping the sample is now offered in Chapter 7 (Sufficiency).

· Revised - Several topics that were discussed in the Exercises are now discussed in the text, including quantiles in Section 1.7.1 and hazard functions in Section 3.3.

Definitions, equations, and theorems are set in bold type help students study more effectively.

New to this Edition

· Many additional real data sets to illustrate statistical methods or compare methods.

o The data sets are also available to students in the free R package hmcpkg. They can also be individually downloaded in an R session at https://media.pearsoncmg.com/intl/ge/abp/resources/products/product.html#product,isbn=9781292264769.

o The R code for the analyses on these data sets are generally given in the text for the students’ benefit.

· Expanded use of the statistical software R, a powerful statistical language which is free and can run on all three main platforms. However, instructors can choose another statistical package if desired.

o Use of R functions is increased to compute analyses and simulation studies, including several games.

o A brief R primer in Appendix B suffices for the understanding of the R used in the text.

· Downloadable, supplemented mathematical review material in Appendix A: reviews sequences, infinite series, differentiation, and integration (univariate and bivariate).

· Expanded discussion of iterated integrals, with added figures to clarify discussion.

· A new subsection on the bivariate normal distribution begins the section on the multivariate normal distribution in Chapter 3 (Some Special Distributions).

· Several important topics have been added, including Tukey’s multiple comparison procedure in Chapter 9 (Inferences About Normal Linear Models)and confidence intervals for the correlation coefficients found in Chapters 9 and 10 (Nonparametric and Robust Statistics).

· Discussion on standard errors for estimates obtained by bootstrapping the sample is now offered in Chapter 7 (Sufficiency).

Several topics that were discussed in the Exercises are now discussed in the text, including quantiles in Section 1.7.1 and hazard functions in Section 3.3.

Table of Contents

• 1. Probability and Distributions
• 2. Multivariate Distributions
• 3. Some Special Distributions
• 4. Some Elementary Statistical Inferences
• 5. Consistency and Limiting Distributions
• 6. Maximum Likelihood Methods
• 7. Sufficiency
• 8. Optimal Tests of Hypotheses
• 9. Inferences About Normal Linear Models
• 10. Nonparametric and Robust Statistics
• 11. Bayesian Statistics
• Appendices:
• A. Mathematical Comments
• B. R Primer
• C. Lists of Common Distributions
• D. Table of Distributions
• E. References
• F. Answers to Selected Exercises
• Index