Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

Probability & Statistics was written for a one or two semester probability and statistics course offered primarily at four-year institutions and taken mostly by sophomore and junior level students, majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus. The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a new chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), expanded coverage of residual analysis in linear models, and more examples using real data.

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.

非参数统计是21世纪统计理论的三大发展方向之一。标准的参数方法强烈地依赖于对数据分布的假设，而非参数方法对模型要求甚少，且更加简单和稳健。随着计算工具的发展，非参数统计模型在许多领域中有越加广泛的应用。非参数统计不仅是统计类学科的必修课，也是统计应用工作者必须掌握的基本方法和思想。 　　本书是作者多年从事非参数统计研究和教学的经验总结。作者用清晰、简洁的语言和丰富的实例，为读者介绍了何时以及如何应用最普遍的非参数统计方法。书后配备了大量意义丰富的习题并附有部分答案。本书为国外众多学校所采用，是一本备受赞誉的非参数统计方面的权威教材。 　　非参数统计为有效地分析试验设计及其实际问题中所获得的数据提供了丰富而有说服力的统计工具，而本书则从问题背景与动机、方法引进、理论基础、计算机实现、应用实例、文献综述等诸多方面介绍了非参数统计方法，其内容包括：基于二项分布的检验、列联表、秩检验、Kolmogorov—Smirnov型统计量等，本书内容丰富、思路清晰、层次分明，在强调实用性的同时，突出了应用方法与理论的结合，书中的正文和习题中都提供了大量的实际案例，书中最后有许多统计用表以及奇数号习题解答和术语索引。 本书作为非参数统计的基础教材，适用于统计学专业的高年级本科生和研究生课程的教学，也可提供给从事统计学应用与研究、数据的分析处理及其相关领域的专业人员阅读与参考。

《统计学:在经济和管理中的应用》(第6版)作者三步走的问题解决方法论把统计问题的解答分解成可控的三个部分：识别、计算、解释。此外，读者还可以到相关网站免费下载CD资源，这一有益的工具为学习统计学提供了新的路径，其内容包括19个看图统计控件、Data Analysis Plus 4．0、Microsoft，Excel插件、学习向导以及以Excel、Minitab、JMP、SPSS与ASCⅡ等格式存储的数据文件。

The relationships （both conceptual and mathematical） between Bayesian analysis and statistical decision theory are so strong that it is somewhat unnatural to learn one without the other. Nevertheless, major portions of each have developed separately. On the Bayesian side, there is an extensively developed Bayesian theory of statistical inference （both subjective and objective versions）. This theory recognizes the importance of viewing statistical analysis conditionally （i.e., treating observed data as known rather than unknown）, even when no loss function is to be incorporated into the analysis. There is also a well-developed （frequentist） decision theory, which avoids formal utilization of prior distributions and seeks to provide a foundation for frequentist statistical theory. Although the central thread of the book will be Bayesian decision theory, both Bayesian inference and non-Bayesian decision theory will be extensively discussed. Indeed, the book is written so as to allow, say, the teaching of a course on either subject separately.