Statistics

• NEW - Three added chapters—Covers nonparametric procedures for the location models and simple linear regression (Ch. 10); a brief introduction to Bayesian methods (Ch. 11); and a comparison of robust and traditional least squares methods for linear models (Ch. 12).

• NEW - Expanded chapter on maximum likelihood procedures—Discusses and applies the EM algorithm to several maximum likelihood situations.

  • Offers students a complete theory of estimation and testing.

• NEW - Extensive discussion of Monte Carlo procedures.

• NEW - Added section on bootstrap confidence intervals and tests.

  • Helps students understand the bootstrap methods used throughout the book.

• NEW - Improved format and presentation—Features many theorems, definitions, and examples in bold faced type.

  • Makes it easier for students to find various items because more definitions, equations, and theorems are given by chapter section, and display numbers.

• NEW - Expanded reference list.

  • Enables students and instructors to find the original source better.

• NEW - Improved organization of content—Features added exercises and examples.

• Thorough treatment of classical statistical inference procedures in estimation and testing.

• In-depth treatment of sufficiency and testing theory—Includes uniformly most powerful tests, and likelihood ratio tests.

• Numerous illustrative examples and exercises.

  • Enhances students' comprehension and retention as they progress through the material.

• Three added chapters—Covers nonparametric procedures for the location models and simple linear regression, Ch. 10; a brief introduction to Bayesian methods, Ch. 11; and a comparison of robust and traditional least squares methods for linear models, Ch. 12.

• Expanded chapter on maximum likelihood procedures—Discusses and applies the EM algorithm to several maximum likelihood situations.

  • Offers students a complete theory of estimation and testing.

• A more complete discussion of Monte Carlo procedures.

• Added section on bootstrap confidence intervals and tests.

  • Helps students understand the bootstrap methods used throughout the book.

• Improved format and presentation—Features many theorems, definitions, and examples in bold faced type.

  • Makes it easier for students to find various items because more definitions, equations, and theorems are given by chapter section, and display numbers.

• Expanded reference list.

  • Enables students and instructors to find the original source better.

• Improved organization of content—Features added exercises and examples.

 1. Probability and Distribution.


 2. Multivariate Distributions.


 3. Some Special Distributions.


 4. Unbiasedness, Consistency, and Limiting Distributions.


 5. Introduction to Inference.


 6. Maximum Likelihood Methods.


 7. Sufficiency.


 8. Optimal Tests of Hypotheses.


 9. Inferences about Normal Models.


10. Nonparametric Statistics.


11. Bayesian Statistics.


12. Comparison of Least Squares and Robust Procedures for Linear Models.


Appendix A. Regularity Conditions.


Appendix B. R-Functions.

Mathematical Statistics  (Statistics)

"The writing style is exceptionally clear; also in the more advanced portion, I haven't any reservations about use. It is a much more professional and modern text than ours. I would seriously consider adopting the text if I teach it again and shall suggest it to my colleagues." — Walter Freiberger, Brown University

"The Hogg/McKean/Craig revision stands out as a modernized version of Hogg and Craig. This revision enhances considerably the statistical inference part with discussion of new procedures and methods, and puts in perspective a broad array of modern statistical methods." — Hosam Mahmoud, George Washington University

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