Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate level course on Mathematical Statistics. It covers all the key topics - statistical models, linear normal models, exponential families, estimation, asymptotics of maximum likelihood, significance testing, and models for tables of counts. It assumes a good background in mathematical analysis, linear algebra, and probability, but includes an appendix with basic results from these areas. Throughout the text, there are numerous examples and graduated exercises that illustrate the topics covered, rendering the book suitable for teaching or self-study.

Features:






A concise yet rigorous introduction to a one-semester course on mathematical statistics
Covers all the key topics
Assumes a solid background in mathematics and probability
Numerous examples illustrate the topics
Many exercises enhance understanding of the material and enable course use

This textbook will be a perfect fit for an advanced course on mathematical statistics or statistical theory. The concise and lucid approach means it could also serve as a good alternative, or supplement, to existing texts.