Introduction to Mathematical Statistics (6e)

Robert V. Hogg, University of Iowa
Allen Craig
Joseph W. McKean, Western Michigan University
Title Introduction to Mathematical Statistics
Edition 6th
ISBN 9780130085078
ISBN 10 0130085073
Published 17/06/2004
Published by P.Ed Heg USA
Pages 692
Format Paperback
In stock
 
Total Price $0.00 Add to Cart
Description

For one or two-semester, undergraduate mathematical statistics course, or for beginning graduate courses in mathematical statistics.

This classic text retains its outstanding features and continues to provide students with excellent background in the mathematics of statistics. Extensively revised with three new chapters.

Table of contents


 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.
New to this edition
  • 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.
Features & benefits
  • 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.