This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level of the book is suitable for a second year graduate course in statistics or biostatistics, provided the students have had a year of graduate level mathematical statistics and a semester of probability.
The book consists of three parts. The first part is a concise overview of all of the main concepts covered in the book with a minimum of technicalities. The second and third parts cover the two respective main topics of empirical processes and semiparametric inference in depth. The connections between these two topics is also demonstrated and emphasized throughout the text. Each part has a final chapter with several case studies that use concrete examples to illustrate the concepts developed so far. The last two parts also each include a chapter which covers the needed mathematical preliminaries. Each main idea is introduced with a non-technical motivation, and examples are given throughout to illustrate important concepts. Homework problems are also included at the end of each chapter to help the reader gain additional insights.
Michael R. Kosorok is Professor and Chair, Department of Biostatistics, and Professor, Department of Statistics and Operations Research, at the University of North Carolina at Chapel Hill. His research has focused on the application of empirical processes and semiparametric inference to statistics and biostatistics. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. He is an Associate Editor of the Annals of Statistics, Electronic Journal of Statistics, International Journal of Biostatistics, Statistics and Probability Letters, and Statistics Surveys.
Michael Kosorok is currently professor and Chair of Biostatistics Department at University of North Carolina Chapel Hill.
The following self description is adopted from his academic website.
I am a composer in my spare time. I have a B.M. in Music Composition from Brigham Young University (1988) and an M.M. in Music Composition from the University of Wisconsin-Madison (1999).
My "Mechanizations" for piano (4 movements, 10 minutes duration) was performed Fall 1995; my "Interactions for Violin and Piano" (4 minutes duration) was performed Spring 1997; and my "Instant Motion" (2 minutes duration) and "February Refractions" (8 minutes duration) for flute, cello, and piano were both performed Spring 1999 in Morphy Hall at the University of Wisconsin-Madison School of Music.
In Spring 2000, my "Eliptical Ascent" (11.5 minutes duration) was performed by the Contemporary Chamber Ensemble in Music Hall at the University of Wisconsin-Madison: the scoring was for flute, oboe, clarinet, bassoon, french horn, trumpet, trombone, percussion, piano, two violins, viola, cello, and double bass.
On December 4, 2007, "A Singular Continuity" for orchestra (about 4 minutes duration) was premiered by the Chapel Hill High School Orchestra under the direction of Barbara Bridges Smith at the Hanes Auditorium in Chapel Hill, North Carolina.
The style of my music is "contemporary classical," or what some people refer to as "new music," and includes works for voice, chamber instrumental groups, orchestra, and percussion.
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读完前几章,我感觉自己仿佛经历了一场智力上的马拉松,而且补给站少得可怜。这本书的组织结构显得有些松散,内容之间的过渡缺乏必要的逻辑桥梁。例如,在介绍了某个关键的随机过程模型之后,下一部分会突然跳跃到非常深入的统计效率分析上,中间缺失了大量本应作为铺垫的直观解释和具体案例演示。我非常希望能够看到一些真实的、来自社会科学或经济学领域的数据集是如何被这些“经验过程”方法处理的,但书中充斥的都是抽象的数学构建,让人很难将理论与实际应用联系起来。这种脱节感严重削弱了阅读的动力。更令人不解的是,很多重要的定义和定理的证明过程被压缩得极其简略,作者似乎默认读者已经掌握了高等概率论的全部知识体系。对于那些希望通过阅读本书来提升实战能力的人来说,这本书的价值主要停留在理论层面,而且是那种最深奥的层面,对于如何“做”推断,它提供的指导非常有限,更像是在描述“推断的数学本质是如此”。
评分阅读这本书的过程,与其说是在学习新知,不如说是在进行一场艰苦的词汇和符号记忆训练。书中的术语使用频率极高,而且很多术语的定义在不同的章节中似乎存在轻微的漂移,这使得读者需要时刻保持高度警惕,以免混淆了细微的差别。例如,对“一致性”的讨论,在描述估计量行为时和描述整个过程收敛时,其数学表达上的细微区分需要多次回溯原文才能确认。我希望作者能够在全书范围内建立起一套统一且明确的符号系统和术语表,并始终如一地遵守它。坦率地说,这本书更像是一部严谨的数学专著,而非一本旨在传授和普及统计推断方法的教学读物。对于那些寻求一本能够清晰、循序渐进地引导他们掌握经验过程精髓的读者来说,他们可能会发现这本书更像是一道难以逾越的学术壁垒,需要大量的外部资源来辅助理解其内在的逻辑脉络。
评分这本书的封面设计简直是色彩和排版的灾难,厚重的纸张握在手里沉甸甸的,仿佛在诉说着内容本身的艰涩。我期待着能找到一些关于统计学基础的直观解释,毕竟书名听起来像是要介绍一些“经验性”的处理方式,但翻开第一页,映入眼帘的却是密密麻麻的希腊字母和积分符号,根本没有为初学者留出任何喘息的空间。作者的写作风格极其学术化,句子冗长且充满了技术性的行话,仿佛是直接从某个高度专业化的会议论文集中摘录出来的段落。我尝试着去理解那些关于“泛函”和“收敛性”的论述,但那就像是试图在浓雾中辨认远处的灯塔,每一个概念都包裹在复杂的数学框架中,难以捉摸。这本书似乎完全没有意识到,即便是最严谨的理论也需要一个友好的入口。它更像是为那些已经对现代统计推断了如指掌的专家准备的工具箱,而非一本引人入胜的入门指南。我花了大量时间试图在前面几章中建立起对核心思想的基本感知,但最终感到的是挫败,因为那些理论的基石似乎从未被清晰地搭建起来。
评分从内容深度来看,这本书无疑是站在了该领域的前沿,但这种深度是以牺牲可读性为代价的。书中对最新研究成果的引用和讨论非常详尽,对于一个已经具备扎实背景的博士生来说,这可能是宝贵的参考资料库。然而,对于那些试图跨越“入门”到“专业”这一鸿沟的人来说,这本书显得过于“专业”了。它没有提供一个渐进式的学习路径。例如,对于那些刚接触到非参数统计学概念的读者,书中对“核函数”的讨论直接进入了关于其光滑性和渐近性质的复杂分析,却从未花笔墨解释为什么选择特定的核函数会影响到估计量的偏差和方差平衡。这种“结论先行、论证跳跃”的叙事方式,让人感觉作者对读者的认知水平预估过高,导致许多关键的“为什么”被忽略了,只留下了“是什么”的数学描述。
评分这本书的排版和图示简直是一场视觉上的折磨。在介绍那些至关重要的收敛性定理时,书中几乎没有使用任何图形化的辅助工具来帮助读者建立空间感或动态理解。每一个图表(如果勉强能称之为图表的话)都像是一个被随意放置的数学公式集合,没有清晰的坐标轴标签,也没有对曲线所代表的物理意义或统计学含义的明确标注。在学习涉及高维空间或函数空间概念时,清晰的视觉辅助是至关重要的,但这本书完全放弃了这种教学方法。这使得我不得不频繁地在纸上画草图,试图重建作者脑海中那些清晰的几何或拓扑结构,这无疑极大地减缓了我的学习进度。一个优秀的教材应该能将复杂的概念转化为易于理解的图像语言,而这本书似乎采取了相反的策略,它将原本就难以理解的概念,进一步用晦涩的视觉呈现方式锁在了象牙塔内,让普通读者望而却步。
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