Fundamentals of Mathematical Statistics

Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate-level course in Mathematical Statistics. It covers all the key topicsstatistical 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. FeaturesA concise yet rigorous introduction to a one-semester course in Mathematical StatisticsCovers all the key topicsAssumes a solid background in Mathematics and ProbabilityNumerous examples illustrate the topicsMany exercises enhance understanding of the material and enable course useThis textbook will be a perfect fit for an advanced course in Mathematical Statistics or Statistical Theory. The concise and lucid approach means it could also serve as a good alternative, or supplement, to existing texts.