Introduction to the Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science)

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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.

Introduction to the Theory of Statistical Inference (Chapman & Hall/CRC Texts in Statistical Science) Review

I had this book as a student, so I have really _studied_ it. This book serves as a really great introduction to statistical inference theory. It is mathematically precise, yet accessible for students who are exposed to the topic for the first time. It contains a large number of carefully worked through examples, which makes the book suitable for self-studies. Self-studies are also made easier by the fact that all exercises have worked through solutions. Concepts that many students find difficult when seeing it for the first time (e.g. Neyman-Pearson test theory) are explained mathematically and with a number of illustrations, making it easier to really grasp the fundamental underpinnings of them. All in all I highly recommend this book - both for teaching the topic and for self-studies.

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