The exercises are grouped into seven chapters with titles matching those in the author's Mathematical Statistics. Can also be used as a stand-alone because exercises and solutions are comprehensible independently of their source, and notation and terminology are explained in the front of the book. Suitable for self-study for a statistics Ph.D. qualifying exam.

This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. This new edition has been revised and updated and in this fourth printing, errors have been ironed out. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are useful in statis...(展开全部)

This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descript...(展开全部)

This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's desc...(展开全部)

John A. Rice 在加州大学伯克利分校获得博士学位，并一直任教于该校统计系，现为该校统计学名誉教授。他是美国数理统计学会成员，发表过多篇理论和应用统计学论文，其研究兴趣集中于海量和需要高强度计算的随机数据的分析方法。

The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Calculus is assumed as a prerequisite, and a familiarity with the concepts and elementary pr...(展开全部)