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. HighlightsBasic notations and ideas of statistical inference are explained in a mathematically rigorous, but understandable, formClassroom-tested and designed for students of mathematical statisticsExamples, applications of the general theory to special cases, exercises, and figures provide a deeper insight into the materialSolutions 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 modelsTheoretical, difficult, or frequently misunderstood problems are markedThe 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.