Statistical inference/ George Casella, Roger L. Berger

Includes bibliography and index

Probability theory -- Transformations and expectations -- Common families of distribution -- Multiple random variables -- Properties of random sample -- Principles of data reduction -- Point estimation -- Hypothesis testing -- Interval estimation -- Asymptotic evaluations -- Analysis of variance and regression -- Regression models

This book 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 are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.