Based on the authors' lecture notes, Introduction to the Theory of Statistical Inference presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Suitable for a second-semester undergraduate course on statistical inference, the book offers proofs to support the mathematics. It illustrates core concepts using cartoons and provides solutions to all examples and problems.


Highlights
- Basic notations and ideas of statistical inference are explained in a mathematically rigorous, but understandable, form

- Classroom-tested and designed for students of mathematical statistics

- Examples, applications of the general theory to special cases, exercises, and figures provide a deeper insight into the material

- Solutions provided for problems formulated at the end of each chapter

- Combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models

- Theoretical, difficult, or frequently misunderstood problems are marked

The book is aimed at advanced undergraduate students, graduate students in mathematics and statistics, and theoretically-interested students from other disciplines. Results are presented as theorems and corollaries. All theorems are proven and important statements are formulated as guidelines in prose. With its multipronged and student-tested approach, this book is an excellent introduction to the theory of statistical inference.

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