Introduction to the Theory of Statistical Inference

by Hannelore Liero, Silvelyn Zwanzig

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 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.

284 pages, Paperback

First published July 20, 2011

Reviews

[Neo · Rating 4 out of 5]

I find this book very informative and provides many examples of statistical models. Besides the concept of open sets, almost all the ideas needed to understand the book is found in a typical introductory class of calculus. Hence, the book is very suitable for someone self-studying statistics, especially if they have taken an earlier class on mathematical statistical. One nice thing about this is book is that they quickly introduce the notion of fisher information and sufficient early on as central notions in understanding statistical inference, unlike a typical undergrad course which would differ them to later parts of the course. They also motivate ideas using examples which is nice.