In this book the author presents with elegance and precision some of the basic mathematical theory required for statistical inference at a level which will make it readable by most students of statistics.
Table of Contents
1. Basic principles of the theory of inference, the likelihood principle, sufficient statistics 2. Distance between probability measures 3. Sensitively of a family of probability measures with respect to a parameter 4. Sensitivity rating, conditional sensitivity, the discrimination rate statistic 5. Efficacy, sensitivity, the cramer-rao inequality 6. Many parameters, the sensitivity matrix 7. Asymptotic power of a test, asymptotic relative efficiency 8. Maximum likelihood estimation 9. The sample distribution function