This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation.
Features
The classic graduate-level textbook on statistical inference
Develops elements of statistical theory from first principles of probability
Written in a lucid style accessible to anyone with some background in calculus
Covers all key topics of a standard course in inference
Hundreds of examples throughout to aid understanding
Each chapter includes an extensive set of graduated exercises
Statistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations.
This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.
1. Probability Theory.
2. Transformations and Expectations
3. Common Families of Distributions
4. Multiple Random Variables
5. Properties of a Random Sample
6. Principles of Data Reduction
7. Point Estimation
8. Hypothesis Testing
9. Interval Estimation
10. Asymptotic Evaluations
11. Analysis of Variance and Regression
12. Regression Models
