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 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. 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.

Steffen Lauritzen is Emeritus Professor of Statistics at the University of Copenhagen and the University of Oxford as well as Honorary Professor at Aalborg University. He is most well known for his work on graphical models, in particular represented in a monograph from 1996 with that title, but he has published in a wide range of topics. He has received numerous awards and honours, including the Guy Medal in Silver from the Royal Statistical Society, where he also is an Honorary Fellow. He was elected to the Royal Danish Academy of Sciences and Letters in 2008 and became a Fellow of the Royal Society in 2011.

本文的引用網址: