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加拿大留学：加拿大十所名校本科申请难度分析
标签： 加拿大大学 加拿大本科申请 加拿大专业
根据往年加拿大大学申请和录取情况，专家为您分析加拿大十所名校本科申请难度，详情如下：
一、安大略省：多伦多大学、约克大学、西安大略大学、滑铁卢大学、麦克马斯特大学
多伦多大学/本科直录申请成功率20%
约克大学/本科直录申请成功率80%
西安大略大学/本科直录申请成功率70%
滑铁卢大学/本科直录申请成功率50%
麦克马斯特大学/本科直录申请成功率60%-
二、BC省：UBC大学、SFU大学、维多利亚大学
UBC大学/本科申请成功率50%
SFU大学/本科申请成功率40%
维多利亚大学/本科直录申请成功率80%
三、魁北克省：麦吉尔大学
麦吉尔大学/本科直录申请成功率25%
四、阿尔伯塔省：阿尔伯塔大学
阿尔伯塔大学/本科直录申请成功率65%
一、安大略省：多伦多大学、约克大学、西安大略大学、滑铁卢大学、麦克马斯特大学
1. 多伦多大学/本科直录申请成功率20%
多伦多大学（University of Toronto）加拿大顶尖大学之一，加拿大常青藤大学，全球TOP30大学，每年申请该大学的中国学生较多且竞争激烈，如学生仅能符合学校最低录取条件，根据经验，往往也只有不到20%录取机会，这个和美国常青藤大学的录取率相仿。专家总结，年度，多伦多大学本科常见申请专业如下：
Bachelor in Chemical and Physical Sciences Mississauga
Bachelor of Commerce (Rotman)
Bachelor of Commerce (Rotman)
Bachelor of Science-Psychology
Bachelor of Physical and Mathematical Sciences(Mathematical Applications in Economics& Finance)/Bachelor of Engineering Science-Math, Statistics and Finance
Bachelor of Science in Computer Science
Bachelor of Phisical and Methematical Science in Financial Economics
Bachelor of Management and Finance
Bachelor of Computer Engineering
Bachelor of Social Science in International Nations
Bachelor of Computer Science
Bachelor of Commerce
Bachelor of Rotman Commerce
Bachelor of Management
Bachelor of Economics, St. George
Bachelor of Computer Science (St. George)
Bachelor of Arts and Science, St. George Campus,University College, Studies in Social Sciences
Bachelor of Science in Physics
Bachelor of Mechanical Engineering
Bachelor of Mechanical Engineering
Bachelor of Commerce in Finance (Mississauga)
Bachelor of Science in Economics and Mathematics
BSc. in Computer Science, Mathematics and Statistics
2. 约克大学/本科直录申请成功率80%
加拿大约克大学位于加拿大第一大城市多伦多市，校生超过50000人，教职员工7000人，约克大学一直以来以工商管理、文科、计算机科学、社会科学等专业驰名。加拿大约克大学由两个校区及两个中心组成，它们分别是：The Keele Campus，Glendon Campus，Miles S. Nadal Management Centre和Osgoode Professional Development Centre。The Keele Campus是约克大学的主校区，占地550英亩，共有全日制本科生33,000名及研究生4,700名，绝大多数的学术项目都在这里进行。Glendon Campus校区面积85英亩，主要是英法双语教学的文科学院，有近2,200名学生在这里学习。Miles S. Nadal Management Centre位于多伦多市的金融区中心，主要设有Schulich商学院。
约克大学常申请专业为BA in Mathematics for Commerce、BA in Financial&Business Economics、BA in Social Science、BBA-Accounting。如果达到录取条件，申请这些专业的成功率80%左右
3. 西安大略大学/本科直录申请成功率70%
UWO加拿大西安大略大学（University of Western Ontario，也称为UWO，目前更名为Western University，而韦仕敦正是Western的音译）坐落在加拿大风景秀美的著名城市伦敦，伦敦市是加拿大第十大城市，人口有34万多人，距加拿大第一大城市多伦多200公里。西安大略大学在校学生超过33,000人，其中有来自120多个国家的留学生约2,200名，全日制教职员工3200人，提供2000多个学士学位与文凭课程。
西安大略大学有超过一百三十多年的学术积累及深厚的人力资源背景，它的商科最为出名，是北美案例法教育的两大发源地之一。除此之外，西安大略大学在其他研究项目也拥有极强的实力。UWO在体表风洞、可替代性能源、小型化工业用引擎、食品营养学等领域上有深厚的造诣，在国际上居领先地位。
根据往年经验，西安大略大学本科录取率较高，超过70%，常申请专业如下：
Bachelor of Management & Organizational Studies_Accounting
Bachelor of Management and Organizational Studies
Bachelor of Civil Engineering
Bachelor of Management Organizational Studies-Accounting
Bachelor of Management and Organizational Studies in Accounting
Bachelor of Accounting
Bachelor of Management & Organizational Studies[Human Resources Management]
Bachelor of Management &Organizational Studies
Ivey-Bachelor of Management Studies in Accounting
BMOS-HR Management
4. 滑铁卢大学/本科直录申请成功率50%
滑铁卢大学waterloo是北美地区最优大学，一直稳居加拿大综合性大学排名的前三位，滑铁卢大学的数学，计算机科学和工程学科教学水平居世界前列。根据以往经验，滑铁卢大学的本科录取率50%左右，常申请的滑铁卢大学的本科专业如下：
Bachelor of Accounting and Financial Management-Business and Finance(WFM)
Bachelor&s program in Actuarial Science/ Electrical Engineering
Bachelor of Computer Science(BSC)(coop)
Bachelor of Computer Science under Faculty of Mathematics
Bachelor of Software Engineering / Bachelor of Mathematics in Actuarial Science
Bachelor of Electronical Engineering
Bachelor of Mathematics(Acturial Science)
Bachelor of Computer Science
Computer science/Mathematics
5. 麦克马斯特大学/本科直录申请成功率60%
麦克马斯特大学McMaster University是加拿大顶尖大学之一，距离多伦多，尼亚加拉大瀑布及美国边境城市约一小时驾车路程，理工科专业十分有名，它的工程类的八个系，任何一个拿出来都能在北美排到前十名。此外，该大学也是加拿大唯一拥有核反应堆的大学。专家介绍，对于国内考生，申请该大学需要高考和会考成绩，6.5，无小分要求，托福86（20），往年经验该大学录取率60%左右，麦克马斯特大学常申请本科专业如下：
Bachelor of Engineering in Electronical Engineering
Bachelor&s program in Mechanical Engineering and Management/Materials Engineering and management
Bachelor of Biotechnology
Bachelor of Mechanical Engineering (B. Eng)
Bachelor of Commerce (Marketing)
Bachelor of Business I in Finance
Bachelor of Business (Operation Management)
Bachelor of Computer Science
Bachelor of Commerce
Bachelor of Mechatronics Engineering
Bachelor of Mechanical Engineering
Bachelor of Materials Engineering
二、BC省：UBC大学、SFU大学、维多利亚大学
1. UBC大学/本科申请成功率50%
UBC大学，全球TOP30大学，加拿大最美大学，加拿大&常青藤&大学，每年申请UBC大学的国内同学最多，但录取率并不低，相比较多伦多大学、皇后大学、麦吉尔大学这些顶尖大学，UBC大学的录取率最高，根据经验，如学生仅能符合学校最低录取条件，并在专业机构的指导下专业申请，录取率可达到50%以上，这个数字远远超过了美国常青藤大学的录取率，因此也是加拿大最好申请的常青藤大学，不过也因此，UBC大学也是加拿大淘汰率较高的大学。专家总结，年度，UBC大学本科常见申请专业如下：
Bachelor of Commerce
Bachelor of Commerce in Accounting（Sauder School of Business)
Bachelor of Science-Geography
Bachelor of Applied Science, Bachelor of Science
Bachelor of Science in Food Nutrition and Health/ Bachelor of Commerce
Bachelor of Commerce in Finance
Bachelor of Commerce in Finance
Bachelor of Business &Computer Science (Bachelor of Commerce),Bachelor of Computer Science(Bachelor of Science)
Bachelor of Commerce
Bachelor of Science , Major Computer Science
Bachelor of Science/ Bachelor of Commerce
Bachelor of Commerce
Bachelor of Arts and Bachelor of Science (Economics)
Bachelor of Economics
BA of Commerce
Bachelor of Computer Science/Mechanical Engineering
Bachelor of Science in Computer Science
Bachelor of Engineering
Bachelor of Finance
Bachelor of Commerce-Accounting(第一选择）；Bachelor of Electronical Engineering(Vancouver)
Bachelor of Commerce(general);Bachelor of Science(general)
Bachelor of Science in Food, Nutrition and Health
Bachelor of Mathematics(Vancouver-BSc)
Bachelor of Applied Science in Material Engineering/Civil Engineering
Bachelor of Commerce in Marketing
Bachelor of Commerce in Finance
Bachelor of Commerce
Bachelor of Mechanical Engineering(Vancuver)
Bachelor of Commerce in Accounting
Bachelor of Commerce/Bachelor of Science
Bachelor of Commerce in Finance
Bachelor of Applied Scince
Bachelor of Mathematical Economics(CAP）
&Bachelor of Commerce-B. Com.
Bachelor of Management -B. Mgt.
Bachelor of Science-Computer Science/Bachelor of Arts-Mathematics
Bachelor of Food and Nutritional Sciences(Vancuver)
Bachelor of Arts in Economics(Vancuver)
2. SFU大学/本科申请成功率40%
UBC/SFU/维多利亚大学，是BC省最好的三所大学，其中西蒙飞沙(SFU)在整个加拿大的注重本科和硕士的大学中位列第一位。每年申请SFU的学生都很多，录取率大约40%左右。申请SFU的中国学生，无需提供高考和会考成绩，直接录取的要求是6.5（6），托福88（20），如果无法达到这个水平，可以考虑选择SFU校内的私立学院FIC，学生可以通过FIC的课程，进入SFU，学生5.5，可以申请FIC读UTP2课程，相当于西蒙菲沙的大一课程，因为是FIC和弗雷泽学院的联合办学，因此UTP2的师资、教材和西蒙菲都是一致的，学生读完UTP2，成绩中上游的学生都能进大SFU读大二。专家介绍，往年申请SFU的专业方向主要包括：
Bachlelor of Science in computer Science
BBA& in Management Information Systems
BA,in Communication
BSC-Computer Science
BSC Acturial Science
BSc in Computer Science
BA in Sociology
Bachelor of Arts
3. 维多利亚大学/本科直录申请成功率80%
加拿大维多利亚大学（University of Victoria）是加拿大省会大学，地处BC省省会维多利亚，校园环境优美，三面环海，校园东行十五分钟是辽阔的海滩公园，是加拿大数一数二注重本科和硕士大学，和UBC、SFU并称加拿大BC省最优秀的三所大学。
VICTORIA要求提高会考，建议提供高考，直接录取要求是6.5（6），托福 90（20），对于未达到语言要求的学生，提供了本科直通车和语言双录取两种方式。专家介绍，维多利亚大学本科录取率高，往年经验，如果达到学校录取标准，录取率80%左右，维多利亚大学本科常申请的本科专业如下：
Bachelor of History in Art
Bachelor of Mechanical Engineering
Bachelor of Business
Bachelor of Social Science in Economics
BSc in Computer Science
Bachelor of Social Science in Environmental Studies
BCom in Accounting
BSc in Statistics
Bachelor of Biology
BCom in Hospitality Management
Bachelor of Social Science in Economics
BSc in Statistics
BA in Sociology
BEng in Mechanical Engineering
BEng in Biomedical Engineering
Bachelor of Enterprese Management Program
Pre-Engineering
Bcom with Sepcialization in Hospitality Services Management
BSc in Health Information Science
三、魁北克省：麦吉尔大学
1. 麦吉尔大学/本科直录申请成功率25%
Mcgill University是加拿大优秀大学的代表之一，和UBC、Toronto、Queen并称加拿大的&常青藤&大学，每年申请Mcgill的国内同学并不多，但竞争十分激烈，如学生仅能符合学校最低录取条件，根据经验，往往也只有不到25%的录取机会，这个和美国常青藤大学的录取率相仿。专家总结，年度，麦吉尔大学本科常见申请专业如下：
Bachelor of Science-Psychology
Bachelor of Science-Biological, Biomedical and Life Sci
Bachelor of Commerce
Bachelor of Electronical Engineering
Bachelor of Science in Architecture
Bachelor of Arts in Economics/ Bachelor of Arts in Art History and Communication Studies(Arth and Coms)
Bachelor of Commerce in Management/ Bachelor of Science in Biomed& Life Science
Bachelor of Arts
Bachelor of Arts-Economics
Bachelor of Economics
Bachelor of Art
Bachelor of math & Computer Science
Bachelor of Architecture/Commerce
Bachelor of Commerce in Accounting
Bachelor of Electrical Engineering
BComm-Economics & Accounting
Bachelor of Economics (Faculty of Arts)
四、阿尔伯塔省：阿尔伯塔大学
1. 阿尔伯塔大学/本科直录申请成功率65%
阿尔伯塔大学（Alberta）本科录取难不难？阿尔伯塔大学是一所百年大学，世界两百强大学，科研力量强，理工科尤其优秀，阿尔伯塔大学提供超过200个本科专业选择，学生可以根据自己的兴趣和特长，选择适合的专业，学到真才实学，此外，阿尔伯塔大学每年学校为国际学生提供的奖学金超过两千一百万加币，非常希望吸收世界各国的优秀学子就读。专家介绍根据往年经验，阿尔伯塔大学本科录取率适中超过65%，学生常选择的本科专业如下：
Bachelor of Statistics/ Bachelor of Design/ Bachelor of Economics
Bachelor of Science in Engineering
BSc in Human Ecology Family Ecology
Bachelor of Science in Engineering
Bachelor of Commerce
Bachelor of Science in Mechanical Engineering
BP Bachelor of Economics
BSc in Engineering -Petroleum Engineering
Science of Engineering
Bachelor of Commerce-AD(Human Resources Mngmt-Maj)
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北美大学一流统计学专业课程设置
北美大学一流统计学专业课程设置摘要： 本文运用质的研究的方法，考察了美国和加拿大的一流大学统计学专业的统计课程设置，并对统计学科进行了分 类，按此类别进行描述；对部分海外留学生和具有国外经历 的教师进行了访谈，再现了他们对国内外课程设置的亲身经 历。一、北美一流统计学专业课程设置大学的选择根据可获得信息的实际情况，本课题主要考察美国的大学。对美国学校的选 择，本文以美国较具权威的网站 所做的数理统计（mathematical statistics）全美排名（1999）的前十名大学和某国内网站的统计（statistics）专业 排名（出处和年度不详）为依据（见附表） 。U.S.News 所进行的排名，主要反映 其学术优势（academic excellence） 。考察各大学的学术声誉、师资力量、学生选 拔、资金状况及学生毕业状况等。通过设计一些客观标准，如：学术界内外的声 誉排名、录取分数、科研经费、从事科研人员的总数和毕业生在职业考试和工作 市场上的表现等；运用专家意见法，请大学的系主任和教员就他们熟悉的项目打 分等方式对不同的专业进行排名。另外一家网站的排名，没有列明出处，据推测 也应源于美国的调查机构，排名虽略有变动，但变化不大，也具有参考价值。因 此，我们认为选取这些大学较具代表性。附表 1：全美数理统计排名 Math Specialties: Mathematical Statistics（1999）1. University of CaliforniaCBerkeley 2. Stanford University (CA) 3. University of WisconsinCMadison 4. University of North CarolinaCChapel Hill 5. Harvard University (MA) 5. University of Chicago 7. Cornell University (NY) 7. Purdue UniversityCWest Lafayette (IN) 9. University of Washington 10. University of MichiganCAnn Arbor附表 2：统计(Statistics)1 Standford Univ. 2 Univ. of California， Berkeley 3 Univ. of Chicago 4 Univ. of Wisconsin， Madison 5 Univ. of North Carolina 6 Cornell Univ. 7 Columbia Univ. 8 Harvard Univ. 9 Iowa State Univ. 10 Princeton Univ. 11 Purde Univ. 12 Univ. of California， Los Angeles 13 Univ. of Illinois at Urbana---Champaign 14 Univ. of Washington，Seattle 15 Yale Univ. 16 Carnegie Mellon Univ. 17 Colorado State Univ.本文综合考虑了两种排名，选取了若干大学的统计学专业进行研究，他们包 括： 加州大学伯克利分校（UC-Berkley） 、哈佛大学、威斯康星大学（Madison） 、 北卡罗来那大学（Chapel Hill） 、芝加哥大学、华盛顿大学、加州大学洛杉矶分 校（UCLA） 、密歇根大学、康奈尔大学、普渡大学（Purdue UniversityCWest Lafayette）等。由于宾夕法尼亚大学的统计学系设于沃顿商学院，因此也比较有 借鉴意义。 加拿大的学校则选取了滑铁卢大学、多伦多大学。课程描述的模式对课程的描述，主要依据《统计学学科体系的构造与完善研究》课题组提出 的理论统计学和应用统计学框架进行的。理论统计学 统计指标体系设计 统计指数理论 试验设计 统计调查 统计描述 概率论 参数估计与假设检验 非参数估计 稳健估计 回归分析 方差分析 应用统计学 统计史学 政府统计 统计思想史学 企业统计 统计活动史学 经济计量学 金融统计 保险精算 人口统计学 社会统计学 科学技术统计 天文统计 地质统计生物统计 生物统计 统计学其他学科 统计活动组织与管理 统计法学 比较统计研究 统计教育与统计培训随机过程 时间序列分析 多元分析 贝叶斯统计 决策论 序贯分析 空间统计 统计计算 理论统计学其他学科气象统计 生态与环境统计 医学与卫生统计 教育与心理计量学 统计质量控制 可靠性分析 生存分析 统计应用软件 应用统计学其他学科具体课程描述统计专业课程设置从某种意义上讲，不同的学校具有其自身的特色；但就其 核心和框架却是大同小异。 我们认为，各学校的课程设置各有侧重，本科生和研究生的差异显著性也有 所不同， 还有另外一个客观原因， 就是不同学校的课程介绍详细程度不同， 因此， 我们以每所学校的某一门课为基本单位，而没有对这些课程内容进行汇总和综 合，从而避免了人为的（即使是无意识的） “扭曲” 。我们将有关课程设置的文本 资料的原貌展现给读者，让读者根据自己的需要去进行加工，我们列出英文的原 文，然后附上中文的翻译i，可能有些翻译不够准确，也有一些术语，国内的翻 译不够统一。 因为这一问题超出了我们的讨论范围， 所以， 其中的译法仅供参考。 为了方便起见，我们设置了查询链接。理论统计学 试验设计 统计调查 统计描述 概率论 参数估计与假设检验 非参数估计 稳健统计 回归分析 方差分析 随机过程 时间序列分析 多元分析 贝叶斯统计 决策论 序贯分析 空间统计 统计计算 理论统计学其他学科 线性模型 数据分析 应用统计学 统计史学 经济计量学 统计思想史学 金融统计 保险精算 人口统计学 社会统计学 科学技术统计 天文统计 地质统计生物统计 生物统计 气象统计 生态与环境统计 医学与卫生统计 教育与心理计量学 统计质量控制 可靠性分析 生存分析 统计应用软件 应用统计学其他学科工商管理统计 统计方法综合课理论统计试验设计（Design of Experiments）先修课：概率论、数理统计、回归分析本科UC-Berkley Experimental Design -- Statistics (STAT) 232 [4 units]Randomization, blocking, factorial design, confounding, fractional replication, response surface methodology, optimal design. Applications.试验设计：随机化，区组设计，析因设计，混杂法, 部分重复, 反应曲面方法, 最 优设计。以及应用。哈佛大学：[Statistics 140. Design of Experiments and Quasi-Experiments] Statistical designs for the estimation of the effects of treatments in both controlled experiments and observational studies. Topics include randomization, blocking, fractional replication, covariance adjustment, subclassification, matched sampling, model-based adjustment.Prerequisite: Statistics 100 and 139, or equivalent.实验和准实验设计： 观察性试验和控制性试验中的处理效果估计的统计设计。 包括：随机化，区组设计，部分重复，协方差调整，次级分类，匹配抽样，基于模型的 调整。滑铁卢大学STAT 332 F,S 3C 0.5 Sampling and Experimental Design Designing sample surveys. Probability sampling designs. Estimation with elementary designs. Observational and experimental studies. Blocking, randomization, factorial designs. Analysis of variance. Designing for comparison of groups.Prereq: STAT 231 or equivalent抽样和实验设计：抽样调查设计。概率抽样设计。基本设计的估计。观察型和实验型研究。 区组设计，随机化，析因设计。方差分析。组间对照的设计。 STAT 430 F,S 3C 0.5 Experimental Design Review of experimental designs in
replication, balance, blocking, randomization, one-way layout, two-way layout, and Latin squ factorial str two-level fraction fixed v split-plot and repeated- other topics. Prereq: STAT 331 and 332, or consent of instructor 实验设计：在回归的背景下概述实验设计；方差分析；重复，平衡法，区组设计，随机化及交互作用；单向布置，双向布置，拉丁方设计；处理的析因结构，共变； 双层部分析因设计；固定效果与随机效果；裂区设计与反复测量设计；及其它论 题。华盛顿大学 STAT 486 Experimental Design (3) NW Topics in analysis of variance and experimental designs: choice of designs, comparison of efficiency, power, sample size, pseudoreplication, factor structure. Prerequisite: Q SCI 482; recommended: Q SCI 483. Offered: jointly with Q SCI 486. 实验设计：方差分析和试验设计专题：设计选择，效率比较，功效，样本规模，伪重复，因 子结构。研究生滑铁卢大学STAT 830 Theory of Experimental Design S,F (0.5) 内容同 STAT 430实验设计理论：同上 芝加哥大学 345 DESIGN AND ANALYSIS OF EXPERIMENTS.An introduction to the methodology and application of linear models in experimental design. A major focus of the course will be the basic principles of experimental design, such as blocking, randomization and incomplete layouts. Many of the standard designs, such as fractional factorial, incomplete block and split unit designs will be studied within this context. The analysis of these experiments will be developed as well, with particular emphasis on the role of fixed and random effects. Time permitting, additional topics may include response surface analysis, robust methods of analysis and the use of covariates in the analysis of designed experiments. PQ Statistics 343 and Statistics 245. Winter.试验设计和分析：试验设计中线性模型的运用和方法介绍。集中介绍试验设计的基本原理， 如：区组设计，随机化和不完整配置。学习许多标准的设计，如：部分析因设计，不完全区 组设计和裂区设计。还将介绍试验分析。 华盛顿大学STAT 577 Advanced Design and Analysis of Experiments (3) Concepts important in experimental design: randomization, blocking, confounding. Application and analysis of data from randomized blocks designs, Latin and Greco-Latin squares, incomplete blocks designs, split-plot and repeated measures, factorial and fractional replicates, response surface experiments. Prerequisite: STAT 570 or STAT 421 (minimum grade 3.0), or permission of instructor. Offered: jointly with BIOST 577.高等实验设计与分析：试验设计中的重要概念：随机化，区组设计，混杂法。随机区组设计 数据的分析和应用，拉丁方和希腊拉丁方，不完整的区组设计，裂区设计和重复测量设计， 析因和部分重复，反应曲面试验。 康奈尔大学 STENGR 575 Experimental Design (Enroll in OR&IE 575.) 2 credits. Weeks 8-14 (alternates with 576). Lecs. TR 8:40-9:55. Secs: W 12:20-2:15, R 2:30-4:25. Prerequisite: OR&IE 476. Instructor: R. Cleary. Randomization, blocking, sample size determination, factorial designs, 2^p full and fractional factorials, response surfaces, Latin squares, split plots, Taguchi designs. Engineering applications. Computing in MINITAB or SAS. 实验设计：随机化，区组设计，抽样规模的确定，析因设计。完全的和部分的析因设计，反 应曲面法，拉丁方，裂区法，Taguchi 设计。工程中的应用。用 MINITAB 或 SAS 计算。 ） 密歇根大学Statistics 570: EXPERIMENTAL DESIGNPrerequisite: Statistics 500, or permission. I. (3) Basic topics and ideas in the design of experiments: randomization and the validity and analysis of ra Latin and Greco-L f the use of confounding and response weighing designs, lattice and incomplete block and partially balanced incomplete block designs.实验设计：试验设计的基本思想和论题：随机化和随机化检验；随机试验的效度与分析；随 机区组设计；拉丁方和希腊拉丁方；制图技术；析因试验，混杂法和反应曲面法的运用；加 权设计，方格设计和不完全区组设计，部分平衡的不完全区组设计。 北卡罗来那大学（下简称北卡大学 ） 194- DESIGN AND ROBUSTNESS Corequisite, Statistics 165. Design: Classical designs (BIB, Latin square, fractional factorial, industrial designs, T Optimal designs: D-optimality, etc.; Sequential designs: sequential probability ratio test, Stein 2-stage. Robust methods: M-, L-, R-estimates, breakdown, bootstrap, jackknife, cross-validation. Fall. Chakravarti, Marron (3) 设计与稳健性：传统的设计：BIB，拉丁方，部分析因设计，工业设计，Taguchi 设计；最 优设计：D-最优化，等；序贯设计：序贯概率比率估计检验，Stein 两阶段法。稳健方法： M-估计，L-估计，R-估计，下分法，影响曲线；bootstrap，刀切法，交叉确认法。211- SPECIAL TOPICS IN THE DESIGN OF EXPERIMENTS Prerequisite, Statistics 150 or 194. Factorial experiments, construction and analysis of symmetrical, mixed, and fractional factorial designs. Orthogonal and balanced arrays. Response surface methodology. Mixture and screening designs. Optimality of designs. Recent developments. (3).试验设计专题：析因设计，对称的，混合的和部分的析因设计的分析和构造。正交数组和平 横数组。反应曲面法。混合和筛选设计。设计的最优化。新近发展。 212- COMBINATORIAL PROBLEMS OF THE DESIGN OF EXPERIMENTS Prerequisite, Statistics 194. Finite groups, fields, and geometries. Difference sets. Orthogonal Latin squares, orthogonal arrays, balanced and partially balanced incomplete block designs. Algebras of association schemes and relations. Randomization, orthogonal designs, general balance and strata. Chakravarti. (3) 试验设计的组合问题：有限总体，现场设计，几何设计。差集。正交拉丁方，正交数组，平衡的和不平衡的不完全区组设计。关联方案和关联关系代数学。随机化，正交设计，一般的 平衡和层。查询统计调查（Sampling Surveys）先修课：数理统计本科UC-Berkley Sampling Surveys -- Statistics (STAT) 152 [4 units] Description: Theory and practice of sampling from finite populations. Simple random, stratified,cluster, and double sampling. Sampling with unequal probabilities. Properties of variousestimators including ratio, regression, and difference estimators. Error estimation for complex samples .统计调查：有限总体抽样理论与实践。随机抽样、分层、整群、二重抽样，不等概抽样，包括比率估计在内的各种估计的性质，回归，差估计（differenceestimators.）、复杂样本的估计误差。哈佛大学[Statistics 160. Survey Methods]Methods for design and analysis of sample surveys. Techniques for sample design, with examples from some widely used current surveys. Estimation methods (including calculation and use of sampling weights) and variance estimation methods (including resampling methods). Several guest lectures on nonstatistical aspects of survey methodology such as questionnaire design and validation. Other topics may include variance estimation for complex surveys and estimators, nonresponse, and small-area estimation.调查方法：抽样调查设计和分析方法。包括抽样设计技术、估计方法（抽样权数的计算和运用）和方差估计法（再抽样方法），无回答问题、非抽样误差。调查 方法的非统计问题客座讲座如：问卷设计与效度。其它问题如：复杂调查和复杂 估计量的方差估计，无回答，小范围估计。威斯康星大学411 An Introduction to Sample Survey Theory and Methods. An elementarydevelopment of the statistical theory (and methods) used to design and analyze the results from sample surveys. Topics: basic tools, simple random sampling, ratio and regression estimation, stratification, systematic sampling, cluster (area) sampling, unequal probability sampling, sampling on successive occasions, non-sampling errors, analytical sample surveys. For illustration and clarification, examples drawn from diverse areas of application.抽样调查理论方法介绍：应用于抽样调查设计和分析的统计理论和方法的基本发展。简单随机抽样、分层随机抽样、比率和回归估计、系统抽样、整群抽样、不 等概率抽样、连续时机抽样、非抽样差和抽样调查分析。用各领域的实际例子进 行说明。宾夕法尼亚大学沃顿商学院210. Sample Survey Design. (M) An overview of survey design and methodology. Topics include questionnaire design, effects of question wording on responses, the sampling frame, simple random sampling, stratified sampling, longitudinal designs and panel methods, data collection, nonresponse bias and missing data, and applications.抽样调查设计：调查设计和方法论综述。 包括：问卷设计，问卷措辞对回答的影响，抽样框，简单随机抽样，分层抽样，纵向设计和 panel 方法， 数据采集， 无回答偏差与缺失数据。密歇根大学Statistics 480: Survey Sampling Techniques Course will introduce students to basic ideas in survey sampling, moving from motivating examples to abstraction to populations, variables, parameters, samples and sample design, statistics, sampling distributions, Horvitz-Thompson estimators, basic sample designs (simple random, cluster, systematic, stratified, multiple state), various errors and biases, special topics. Three hours lecture and 1.5 hour laboratory session each week.调查抽样技术：向学生介绍抽样调查的基本思想，从引人入胜的例子抽象出总体、变量、参数、样本和抽样设计，抽样分布。Horvitz-Thompson 估计，基本的抽样设计（简单随机抽样、整群抽样、系统抽样、分层抽样、多阶段抽样），各种 误差和偏差。滑铁卢大学STAT 454 W 3C 0.5Sampling Theory and PracticeSources of survey error. Probability sampling designs, estimation and efficiency comparisons. Distribution theory and confidence intervals. Generalized regression estimation. Software for survey analysis.抽样理论和实践：调查误差的来源。概率抽样设计，估计和效率比较。分布理论和置信区间。广义回归估计。调查分析软件。研究生滑铁卢大学STAT 854 Sampling Theory and Practice W (0.5) Sources of survey error. Probability sampling designs, estimation and efficiency comparisons. Distribution theory and confidence intervals. Generalized regression estimation. Software for survey analysis.抽样理论和实践：内容同本科。芝加哥大学 331 SAMPLE SURVEYS (= Sociology 368). This course develops the classicaltechniques of Sample Surveys - random sampling methods, stratification, cluster sampling, ratio estimation, and elaborations of these ideas (systematic sampling, regression estimation) - with due attention to derivations and limitations. Methods for dealing with non-response and partial response (call-back schemes, imputation methods) will also be addressed. PQ Consent of Instructor. Autumn.抽样调查：该课介绍抽样调查的传统技术――随机抽样方法，分层抽样，整群抽 样，比率估计以及这些方法的深入（系统抽样，回归估计）――方法的推广和局 限。无回答和部分回答问题的处理（在访问计划和迭代方法）。威斯康星大学 611 Sample Survey Theory and Method.Si strati ratio and
subsampling with units of eq multi-stage and multi- Bayesian and other approaches.抽样调查理论与方法：简单随机抽样、分层随机抽样、比率和回归估计、系统抽样、等样本容量和不等样本容量次级抽样；双重抽样、 多阶段和多相抽样，贝叶斯 及其它方法。密歇根大学Statistics 580 (Biostat 617, Soc 717): THEORY OF SAMPLING Mathematical foundations of sampling finite populations. Si ratio and
systematic sampling, cost functions and choi estimation procedures.抽样理论：有限总体抽样的数学基础。简单随机抽样，分层抽样、比率和回归估计，系统抽样、次级抽样，成本函数与最优设计选择；估计过程。Statistics 670: INTERMEDIATE SAMPLING THEORYRecent developments in the foundations and methodology of sampling finite populations. Identifiability of units, likelihood of units, likelihood functions, admissibility of standard estimators, randomization, use of prior information in design and inference. Models for non-sampling errors including bias, response error and non-response. Other topics of current interest.中级抽样理论：有限总体抽样的基础和方法论的最新发展。单位的识别，单位被抽中的可能性（likelihood of units,），似然函数，标准估计量的容许度，随机性、设计和推断中先验信息的使用，包含偏差、回答误差和无回答误差的非抽样误差 模型。 北卡大学225- SUBSAMPLING TECHNIQUES Prerequisite, Statistics 165. Basic subsampling concepts: replicates, empirical c.d.f., U-statistics. Subsampling for i.i.d. data: jackknife, typical-values, bootstrap. Subsampling for dependent or nonidentically distributed data: blockwise and other methods. Carlstein. (3).次级抽样：次级抽样基本概念；重复抽样1，经验 c.d.f，U 统计量。i.i.d.数据的 次级抽样：刀切法，典型值，bootstrap。非独立或分布不一致数据的次级抽样： 顺时针法或其他方法。华盛顿大学STAT 403 Introduction to Resampling Inference (4) NW Introduction to computer-intensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap resampling, rerandomization, and subsampling of cases.再抽样（Resampling）推断介绍：介绍经验科学中试验性研究和观测性研究的计算机密 集型数据的分析。应用 bootstrap 再抽样，再随机化和次级抽样的案例，学生进行设 计、编程，实施并完成报告。STAT 529 Sample Survey Techniques (3) Design and implementation of selection and estimation procedures. Emphasis on human populations. Simple, stratified,
multistage and two- optimal all national samples and census materials. Prerequisite: STAT 421, STAT 423, QMETH 500 or BIOST 511 or permission of instructor.参见：L.Kish 著，倪加勋主译《抽样调查》 ，中国统计出版社，1997 年 12 月。该书中将 replicated sampling 译为“重复抽样” 。1抽样调查技术：选样和估计过程的设计和实施。侧重于人口调查。简单抽样、分层成员、整群抽样；多阶段和两相抽样；资源的最优配置；估计理论；重复抽样 设计；方差估计；全国抽样和普查资料。查询概率论（Theory of Probability）先修课：微积分、线性代数本科UC-Berkley Introduction to the Theory of Probability -- Statistics (STAT) 101 Description: Random variables and their distributions, expectation, univariate models, central limit theorem, statistical applications, dependence, multivariate normal distribution, conditioning, simulation, and other computer applications. 概率论导论：随机变量及其分布，期望，单变量模型，中心极限定理，统计应用，非独立性，多元正态分布，建立条件（conditioning,），模拟，其它计算机应用。Concepts of Probability -- Statistics (STAT) 134 Description: An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.概率思想：概率论介绍，强调思想和应用。条件期望、独立性、大数法则，离散的和连续的随机变量，中心极限定理，选择性内容：泊松过程、马尔可夫链、特 征函数。Probability Theory -- Statistics (STAT) 205A Prerequisites: Some knowledge of real analysis and metric spaces, including compactness, Riemann integral. Knowledge of Lebesgue integral and/or elementary probability is helpful, but not essential, given otherwise strong mathematical background. Description: Measure theory concepts needed for probability. Expectation, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Cond martingales and theory convergence. Markov chains. Stationary processes.概率论：概率论所需的测度论概念。期望，分布。大数法则和中心极限定理。特征函数方法。条件期望；鞅和理论收敛。马氏链。平稳过程。宾夕法尼亚大学沃顿商学院 430. Probability. (C) Staff. Prerequisite(s): MATH 141 or equivalent.Discrete and continuous sample sp random variables, distributions, expectation and
Markov chains and recurrence theory.概率论：离散的和连续的抽样空间和概率，随机变量，分布，独立，期望和生成函数马氏链 和常返理论。 密歇根大学 Statistics 425/Mathematics 425: Introduction to ProbabilityBasic con expectation, variance, di andbivariate, marginal, and conditional distributions.概率论引论：概率的基本概念；期望、方差、协方差、分布函数、二元分布、边 缘分布和条件分布。Statistics 430: Applied ProbabilityPrerequisites: Statistics 425 or equivalent. (4). II. (MSA).Review of probability introduc counting and P Markov chains in discrete equations for stat introduction to Brownian motion. Selected applications such as branching processes, financial modeling, genetic models, the inspection paradox, inventory and queuing problems, prediction, and/or risk analysis. Selected topics such as hidden Markov chains, martingales, renewal theory, and/or stationary process. Three hours lecture each week.应用概率：概率论综述；随机游走；计数和泊松过程；离散和连续时间上的马尔可夫链；平稳分布方程；布朗运动介绍；应用：分枝过程，金融建模，基因模型， 存贮问题和排队问题，预测，风险分析。选择性论题，如：隐性马氏链，鞅，更 新理论，平稳过程。华盛顿大学 STAT 394 Probability I (3) NW S basic a combi conditional probabil binomial, Poisson and normal distributions, central limit theorem. Prerequisite: either 2.0 in MATH 126, 2.0 in MATH 129, or 2.0 in MATH 136; recommended: MATH 324 or MATH 327. Offered: jointly with MATH 394; AWS.概率论 I：样本空间； 概率论基本定理； 组合概率； 条件概率与独立性； 二项分布，泊松分布和正态分布。中心极限定理。STAT 395 Probability II (3) NW R expe l normal approximation and
multidimensional distributions and transformations. Prerequisite: STAT/MATH 394. Offered: jointly with MATH 395;概率论 II：随机变量；期望和方差；大数法则；正态近似和其他极限定理；多维分布和变换。STAT 396 Probability III (3) NW Characteristic functions and
recurrent event random walk. Prerequisite: either 2.0 in MATH 395 or 2.0 in STAT 395. Offered: jointly with MATH 396; Sp.概率论 III：特征函数和生成函数；常返事件和更新理论；随机游走。研究生UCLA M220A-M220B. Applied Probability. (4-4) Lecture, three hours. Requisite: course M100A or Mathematics M170A. S/U or letter grading. M220A. Conditioning, Markov chains, Poisson process, Brownian motion, stationary processes, applications. M220B. Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology.应用概率论：（A）马氏链、泊松过程、布朗运动、平稳过程及其应用。（B） 模拟、更新理论，鞅，及选择性论题如：排队论，可靠性，语音识别，计算生物 学，数理金融，流行病学。宾夕法尼亚大学沃顿商学院 430. Probability. (C) Staff. Prerequisite(s): MATH 141 or equivalent. Discrete and continuous sample sp random variables, distributions, expectation and
Markov chains and recurrence theory. 概率论：离散的和连续的抽样空间和概率，随机变量，分布，独立，期望和生成函数马氏链 和常返理论。 530. Probability. (A) Steele. Prerequisite(s): STAT 430 or 510 or equivalent.Measure theory and foundations of Probability theory. Zero-one Laws. Probability inequalities. Weak and strong laws of large numbers. Central limit theorems and the use of characteristic functions. Rates of convergence. Introduction to Martingales and random walk.概率论（A） ：测度论和概率论基础。0-1 律。概率不等式。强、弱大数定律。中心极限定理 和特征函数应用。收敛律。鞅和随机游走介绍。531. Probability. (B) Steele. Prerequisite(s): STAT 530. Markov chains, Markov processes, and their limit theory. Renewal theory. Martingales and optimal stopping. Stable laws and processes with independent increments. Brownian motion and the theory of weak convergence. Point processes.概率论（B） 马尔可夫链，马尔可夫过程，及其极限理论。更新理论。鞅，最优 ：停步。 独立增量过程和稳定法则。布朗运动和弱收敛力量。点过程。900. Advanced Probability. (M) Staff. Prerequisite(s): STAT 531 or equivalent. The topics covered will change from year to year. Typical topics include the theory of large deviations, percolation theory, particle systems, and probabilistic learning theory.高等概率论：大偏差理论，渗透理论，粒子系统，概率学习理论。UC-Berkley Introduction to Probability and Statistics at an Advanced Level -- Statistics (STAT) 200A [4 units] Description: Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. 高等概率论与统计：概率空间，随机变量、概率分布与统计分布、中心极限定理、泊松过程、 随机变量的变换、估计、置信区间、线性模型假设检验、大样本理论、类别模型、决策论。 密歇根大学 Statistics 525 (Math 525): PROBABILITY THEORYPrerequisites: Mathematics 450 or 451, or permission. (3)This course covers basic topics in probability, including: random variables, distributions, conditioning, independence, expectation and generating functions, special distributions and their relations, transformations, non-central distributions, the multivariate normal distribution, convergence concepts, and limit theorems. Other possible topics: random walks, Markov chains, martingales.概率论：随机变量，分布，条件，独立，期望和生成函数，特殊分布及其联系， 变幻，非中心分布，多元正态分布，收敛观念，极限定理。随机游走，马尔可夫 链，鞅。Statistics 620: THEORY OF PROBABILITY IPrerequisite: Mathematics 451 or equivalent. I. (3)Basics of probability at an advanced level. Specific topics include: discrete probability spaces, the weak law of large numbers, the de Moivre-Laplace theorems, classes of sets, algebras, measures, extension of measures, countable additivity and Lebesgue and product measures. Also: measurable functions, random variables, conditional probability, independence, the Borel-Cantelli lemmas and the zero-one law. The course will additionally cover: integration, convergence theorems, inequalities, Fubini's theorem, the Radon-Nikodym theorem, distribution functions, expectations, and the strong law of large numbers.概率论 I：离散的概率空间， 弱大数定律，Moivre-Laplace 理论, 集合的类, 代 数, 测度,测度的扩张， 可列可加性和 Lebesgue 测度及乘积测度。也包括：可测 函数，随机变量，条件概率，独立， the Borel-Cantelli lemmas 和 0-1 律。还包 括： 积分, 收敛理论，不等式，Fubini's 理论， Radon-Nikodym 理论，分布函 数、期望和强大数定律。Statistics 621: THEORY OF PROBABILITY IIPrerequisite: Statistics 620. II. (3)A continuation of Statistics 620. Topics covered include: weak convergence, characteristic functions, inversion, unicity and continuity, the central limit theorem for sequences and arrays aud, extensions to higher dimensions. Also: the renewal theorem, conditional probability and expectation, regular conditional distributions, stationary sequences aud the bergodic theorem, martingales, and the optimal stopping theorem. The course will also cover: the Poisson process, Brownian motion, the strong Markov property and the invariance principle.概率论 II：概率论 I 的继续课。包括：弱收敛，特征函数，逆转，单一性和连续性，序列和排列的中心极限定理及其高维扩展。更新理论，条件概率和期望，规 则的条件分布，平稳序列和 bergodic 定理，鞅，最优停步理论。泊松过程，布 朗运动，强马尔可夫性和不变原则。Statistics 628, 629: PROBABILITYPrerequisite: Statistics 625. 628, I; 629, II. (3 each)Special topics in probability theory - for example: infin founda stochastic differential equations. The course(s) will study a few topics in detail.概率论： 概率论专题，如：无限可分法则；时间序列基础；扩散过程；随机微分方程。该课将详细学习一些专题。Statistics 630: TOPICS IN APPLIED PROBABILITYPrerequisite: Statistics 526 or Statistics 626. I. (3)Advanced topics in applied probability, such as queueing theory, inventory problems, branching processes, stochastic difference and differential equations, etc. The course will study one or two advanced topics in detail.应用概率论专题：应用概率高级专题，如：排队论，存贮问题，分枝过程，随机 微分和差分方程等。本课将详细学习一到两个专题。芝加哥大学304. Distribution Theory. PQ: Stat 245 and Math 205, or consent of instructor.This course covers methods of deriving, characterizing, displaying, approximating, and comparing distributions. Topics include algebra by computer (Maple and Macsyma), standard distributions (uniform, normal, beta, gamma, F, t, Cauchy, Poisson, binomial, and hypergeometric), moments and cumulants, characteristic functions, exponential families, the Pearson system, Edgeworth and saddlepoint approximations, and Laplace's method. Staff. Autumn.分布理论：包括：分布的推导，特征化，展示，近似和比较。专题包括：计算机代数（(Maple 和 Macsyma） ，标准分布（均匀分布，正态分布，β分布，Γ分布，F 分布，t 分布，柯西分 布，泊松分布，二项分布和超几何分布） ，矩和累差，特征函数，指数族，皮尔森系统， Edgeworth 逼近法，鞍点逼近法和 Laplace 法。381. Measure-Theoretic Probability I. PQ: Stat 313 or consent of instructor. A detailed, rigorous treatment of probability from the point of view of measure theory, as well as existence theorems, integration and expected values, characteristic functions, moment problems, limit laws, Radon-Nikodym derivatives, and conditional probabilities.测度概率论： 从测度论的角度研究概率论， 存在理论， 积分和期望值， 特征函数， 矩问题，极限法则，Radon-Nikodym 导数，条件概率。北卡大学231- ADVANCED PROBABILITY Prerequisites, Statistics 154 and 155. Advanced theoretic course covering topics selected from: weak convergence theory, central limit theorems, laws of large numbers, stable laws, random walks, martingales. (3).高等概率论：弱收敛理论，中心极限定理，大数定律，稳定法则，随机游走，鞅。 234- EXTREME VALUE THEORYPrerequisites, Statistics 154 and 155. Classical asymptotic distributional theory for maxima and order statistics from i.i.d. sequences, including extremal types theorem, domains of attraction, Poisson properties of high level exceedances. Extremal properties of stationary stochastic sequences and continuous time processes. Leadbetter. (3)极值理论：i.i.d.序列顺序统计量和极大统计量的传统渐进分布理论，包括极值类 型理论，吸引的范围，高层次超过数的泊松性质。平稳随机序列和连续时间过程 的极值性质。华盛顿大学STAT 521 Advanced Probability (3) Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Prerequisite: MATH 426. Offered: jointly with MATH 521;高等概率论：测度论和积分，独立性，大数法则。分布的 Fourier 分析，中心极限问题和无限可分法则，条件期望，鞅。康奈尔大学STENGR 769 Topics in Markov Chains (Enroll in OR&IE 769.) 3 credits. Lecs: TR 1:25-2:40. 1) Review of countable state space Markov chain theory. 2) Markov chainson general state spaces. Transition frunction, Hitting times, irreducibility, C-set lemma, Harris recurrence, regeneration, renewal equations, invariant measures, positive recurrence, aperidicity, the basic ergodic theorem, ration limit theorems, applications. 3) Markov chains in metric spaces. Feller property, occupation measures, invariant measures, Foster-Lyaponov crieteria, applications. 4) Markov chains and i.i.d. random iteration of maps. IID random maps,measurability and Markov property, Kiefer theorm, Lipschitz maps and log contractivity, random affine maps, Dubins Freedman theorem on random monotone maps, Bhattacharya-Majumdar theorem, perfect sampling and Doeblin chains. 5) MCMC (Markov Chain Monte Carlo), Gibbs sampling, conditions for convergence, conditionals and joint distributionsk, improper priors. 6) Random logistic maps. Definition, review of the deterministic case, invariant measures for the random case, uniqueness and convergence to stationarity, nonuniqueness example, applications in economics and ecology. No exams. Project work involving reporting on recent research may be assigned.马尔可夫链专题：1）可数状态空间马尔可夫链理论。 2）一般状态空间上的马尔可夫链。 传递函数，Hitting times，不可约性，C-集合引理，Harris 常返理论，再生理论，更新方程，正的常返状态，非周期性，基本遍历理论，比率极限理论，及其应用。3）矩阵空间上的马尔可夫链。Feller 性质，占有测度，不变测度，Foster-Lyaponov条件及其应用。 4）马尔可夫链和 i.i.d 地图随机迭代。IID 随机地图，可测性和 马尔可夫性质，Kiefer 理论， Lipschitz 地图，对数收缩性，随机仿射地图，随 机单调地图的 Dubins Freedman 理论，Bhattacharya-Majumdar 理论，完美抽 样与 Doeblin 链。 5）MCMC (Markov Chain Monte Carlo), Gibbs 抽样，收敛 条件，条件分布和联合分布，假先验信息。 6）随机逻辑图。定义，确定性情况 的回顾，随机案例的不变测度，平稳性的收敛和唯一性，非唯一的例子，在经济 学和生态学中的应用。哈佛大学 Statistics 311. Recent Advances in Markov Chain Monte Carlo Technology Catalog Number: 0826David van Dyk 2669 Half course (fall term). Th., 2C 4.Starting with a review of such standard techniques as Data Augmentation, the Gibbs sampler, and Metropolis Hastings, the course will focus on recent research papers on such topics as adaptive rejection sampling, the method of auxiliary variables, simulated tempering, thecollapsed Gibbs sampler, marginal and conditional data augmentation, the nested EM algorithm, slice sampling, exact sampling, simulated sintering, reversible jump MCMC, regeneration, and sequential MC methods. Prerequisite: Statistics 220 or equivalent.MCMC 技术的新进展：数据增广，Gibbs 抽样，和 Metropolis Hastings 等标准 技术的回顾，课程集中介绍近期论文，如：自适应拒绝抽样，辅助变量方法，模 拟协调（模拟回火），衰弱的 Gibbs 抽样，边际的和条件的数据增广，嵌套 EM 算法 ，片抽样，精确抽样，模拟烧结，可逆的跳跃 MCMC 算法，再生，序贯 MC 方法。 查询参数估计与假设检验（Parameter estimation and hypothesis testing）先修课：微积分，线性代数。有些学校单独开设此课，大部分在数理统计（Mathematical Statistics），统计 推断（Statistical Inference），统计方法（Statistical Methods）等课中涉及。本科哈佛大学Statistics 111. Introduction to Theoretical Statistics Basic concepts of statistical inference from frequentist and Bayesian perspectives. Topics include maximum likelihood methods, confidence and Bayesian interval estimation, hypothesis testing, least squares methods, and analysis of variance. Prerequisite: Statistics 110 and basic linear algebra.理论统计介绍：从频度和贝叶斯角度介绍统计推断的基本概念。包括：极大似然估计法，置 信区间估计和贝叶斯区间估计，假设检验，最小二乘法，方差分析。Statistics 211. Probability Theory and Statistical Inference II Introduction to statistical inference. Frequency, Bayesian, and decision-theoretic approaches. Likelihood, sufficiency, multivariate Normal distribution, and exponential families. Testing hypotheses and estimation. Maximum likelihood estimation, likelihood ratio tests, linear models, models for frequency data, large and moderate sample approximations, including the delta method.Prerequisite: Advanced calculus, Statistics 210, or equivalent.概率论与统计推断：统计推断介绍。频数，贝叶斯方法和决策论。似然性，充分 性，多元正态分布，指数分布族。假设检验和估计。极大似然估计，似然比率检 验，线性模型，频度数据模型，大样本和一般样本近似，含 delta 法。BIO 231cd. Statistical Inference I 5 credits Lectures, laboratories. Two 2-hour sessions each week. One 1.5-hour lab each week. A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.统计推断：统计推断基础。数据整理的一般原则：指数分布族，充分统计量，辅 助统计量，完整统计量。点估计和区间估计的一般方法，估计量的小样本和大样 本性质：矩法，极大似然估计，无偏估计，Rao-Blackwell 和 Lehmann-Scheffe 理论，信息不等式，渐进相对有效估计量。假设检验和经验的最有性质： Neyman-Pearson 理论，似然比率估计，score 和 Wald 经验，一致最大功效检 验，局部最大功效检验，检验的渐进相对有效性。滑铁卢大学STAT 450 W 3C 0.5 Estimation and Hypothesis Testing Discussion of general inference problems under the headings of point and interval estimation, hypothesis testing and decision theory. Large sample normal likelihoods, maximum likelihoodestimation, theory of UMV estimation, least squares, Neyman-Pearson theory of hypothesis testing.估计和假设检验：点估计和区间估计下的一般推断问题，假设检验和决策论。大 样本正态似然性， 极大似然估计， UMV 估计理论， 最小二乘法， Neyman-Pearson 假设检验理论。研究生宾夕法尼亚大学431. Mathematical Statistics. (C) Cai, Staff. Prerequisite(s): STAT 430. Special distributions, testing hypotheses, estimation, empirical distributions, sampling, correlation and regression, and goodness of fit.数理统计：特殊分布，假设检验，估计，经验分布，抽样，相关和回归，拟合优 度。550. (BSTA622) Mathematical Statistics. (A) Brown. Prerequisite(s): STAT 431 or 511 or equivalent. Decision theory and statistical optimality criteria, sufficiency, invariance, estimation and hypothesis testing theory, large sample theory, information theory.数理统计：决策论和统计最优条件，充分性，不变性，估计和假设检验理论，大 样本理论，信息理论。北卡大学 164- STATISTICAL THEORY I Prerequisite, two semesters of advanced calculus. P Random variables, distributions, C G Limit theorems: LLN, CLT, Slutzky, -method, big- I Distribution theory: normal, chi-squared, beta, gamma, Cauchy, other multiv Distribution theory for linear models. Fall. Simons. (3)统计理论 I：概率空间；随机变量，分布，期望；条件；母函数；极限定理：LLN, CLT, Slutzky 方法，大概率；不等式；分布理论：正态。卡方，β 分布，γ 分布， 柯西分布，其它多元分布；线性模型的分布理论。165- STATISTICAL THEORY II Prerequisite, Statistics 164 or equivalent. P Hypothesis testing Contingency tables, nonparametric goodness-of- Linear model optimality theory: BLUE, MVU, MLE; M Introduction to decision theory and Bayesian inference. Ji, Marron, Simons. Spring. (3)统计理论：点估计；假设检验和置信集；列联表，非参数拟合优度；线性模型优 化理论：BLUE, MVU, MLE；多元检验；决策论和贝叶斯推断。220- ESTIMATION, HYPOTHESIS TESTING, AND STATISTICAL DECISION Prerequisites, Statistics 155 and 165. Bayes procedures for estimation and testing. Minimax procedures. Unbiased estimators. Unbiased tests and similar tests. Invariant procedures. Sufficient statistics. Confidence sets. Large sample theory. Statistical decision theory. Simons. (3).估计， 假设检验和统计决策： 贝叶斯估计和经验方法。 极小极大程序。 无偏估计。 无偏检验和相似检验。不变估计程序。充分统计量。置信集。大样本力量。统计 决策力量。224- STATISTICAL LARGE SAMPLE THEORY Prerequisites: Statistics 155 and 165 Asymptotically
maximum likelihood estimators. Asymptot likelihood ratio tests. Simons. (3).统计大样本理论：渐进有效估计量；极大似然估计量。渐进最优检验；似然比率检验。密歇根大学Statistics 664, 665: ASYMPTOTIC METHODS IN STATISTICS I & II Prerequisite: Statistics 625 or permission. 664, I; 665, II. (3 each)Advanced topics in large sample theory. Some possible topics include: The invariance principle
asymptotic distributions of li asymptotic distributions of linear combinations inference based on asymptotic properties of likelihood functions and posterior distributions.统计中的渐进方法：大样本理论的高级专题。包括：不变原理及其应用；线性秩 统计量的渐进分布；顺序统计量的线性组合的渐进分布；基于似然函数和后验分 布的渐进性质的统计推断。滑铁卢大学STAT 850 Estimation and Hypothesis Testing W (0.5) The frequency approach to assessing inference, estimating equations and pivotal quantities, comparison of estimates, asymptotic properties of the likelihood, tests of hypothesis, tests of significance.估计和假设检验：评价推断的频率方法，估计方程和关键量，估计量的比较，概 率的渐近性，假设检验，显著水平检验。密歇根大学Statistics 510: MATHEMATICAL STATISTICS I Prerequisites: Mathematics 450 or 451 and a course in probability or statistics at least at the level of Stat. 425/426, or permission. I (3) A brief review of probability theory. Introduction to decision theory including: models, parameter spaces, decision rules, and risk functions. Likelihood and sufficiency principles. Estimation theory including: group families and exponential families, unbiasedness, completeness and sufficiency, Lehmann-Scheffe and Rao-Blackwell information inequality, bias reduction techniques, a brief introduction to bootstrap/jackknife, Bayes rules, minimax rules, empirical Bayes procedures and James-Stein estimators. Large sample theory including: consistency, efficiency, asymptotic normality and applications.数理统计 I：概率论概述。决策论介绍：模型，参数空间，决策法则，风险函数。 似然性和充分性原则。估计理论： group 族和指数族，无偏性，完备性和充分 性， Lehmann-Scheffe 和 Rao-Blackwell 信息不等式, 偏差缩减技术, bootstrap/jackknife 法简介, Bayes 法则，最大风险最小化原理，经验贝叶斯方 法和 James-Stein 估计量。 大样本理论： 一致性， 有效性和渐进正态性及其应用。Statistics 511: MATHEMATICAL STATISTICS IIPrerequisite: Statistics 510 or permission. II (3) The theory of hypothesis testing including: tests, significance levels, power, the Neyman-Pearson lemma, uniformly most powerful unbiased tests,monotone likelihood ratios, locally best tests, invariant/similar tests, likelihood ratio tests and associated large sample theory, goodness of fit tests, and tests in contingency tables. Other topics include: confidence regions, introduction to general linear models, rank tests, and special topics. 数理统计 II：假设检验理论：检验，显著性水平，功效， Neyman-Pearson 引 理，一致最大功效无偏检验，单调似然比率，局部最优检验，不变/相似检验， 似然比率检验和相关大样本理论，拟合优度检验，列联表检验。其它包括置信区 间，广义线性模型介绍，秩检验。华盛顿大学STAT 581 Advanced Theory of Statistical Inference (3)Limit theorems, asymptotic methods, asymptotic efficiency and efficiency bounds for estimation, maximum likelihood estimation, Bayes methods, asymptotics via derivatives of functionals, sample-based estimates of variability: (bootstrap and jackknife); estimation for dependent data, nonparametric estimation and testing. Prerequisite: STAT 513 and MATH 426. Offered: A. 高级统计推断理论：极限理论，渐进方法，渐进有效和估计的效率，加大似然估 计，贝叶斯方法，泛函数的微商渐进性，基于样本的估计：（bootstrap 和 jackknife 方法）；稳健性；独立数据的估计，非参数估计和经验。康奈尔大学 STMATH 674 Introduction to Mathematical Statistics (Enroll in MATH 674.) 4 credits. Lecs: TR 2:55-4:10. Prerequisite: Mathematics 671 and OR&IE 670 or permission of instructor. Instructor: M. Nussbaum. Topics include an introduction the the theory of point estimation, hypothesis testing and confidence intervals, consistency, efficiency, sufficiency, and the method ofmaximum likelihood. Basic concepts of decision
asymptotic methods are introduced and developed in detail. The course is coordinated with OR&IE 670 to form the second part of a one-year course in mathematical statistics. 数理统计引论：包括点估计理论，假设检验和置信区间，一致性，有效性，充分性，极大似 然法。决策论基本概念；渐进方法。哈佛大学 [Statistics 214. Causal Inference in Statistics and the Social and Biomedical Sciences ] Approaches to causal inference. Covers randomized experiments with and without noncompliance, observational studies with and without ignorable treatment assignment, instrumental variables and sensitivity analysis. A number of applications from economics, medicine, education, etc., are discussed.Note: Expected to be given in 2000C 01.统计学、社会学和生物医学中的因果推断：因果推断的方法。非可塑性和可塑性 的随机试验，带有和不带有可忽略处置的观察性研究，有助变量和敏感性分析。 经济、医学和教育学中的应用。 华盛顿大学 STAT 566 Causal Modeling (3-5, max. 15) Construction of causal hypotheses. Theories of causation, counterfactuals, intervention vs. passive observation. Contexts for causal inference: ra sequ natural experiments, passive observation. Path diagrams, conditionalindependence and d-separation. Model equivalence and causal under-determination. Prerequisite: course in statistics. Offered: jointly with CS&SS 566.因果建模：因果假设的构造。因果论，反事实，干预与被动观察。因果推断的背景：随机试 验；序贯随机化；部分可塑性；自然试验，被动观察。路径图，条件独立和 d-分隔。模型 等价和因果弱确定性。 查询非参数估计 （Nonparametric estimation）本科威斯康星大学351 Introductory Nonparametric Statistics. I; 3 cr (N-A).Distribution free statistical procedures or methods valid under nonrestrictive assumptions: order statistics, distribution free tests and associated interval
Mann Whitney W Kolmogorov S methods for discrete data computer tec discussion and comparison with parametric methods. P: Stat 201 or 301 or 224 or cons inst.非参数统计引论：无限制性假定时的无分布统计方法：基本工具，计数法，顺序统计量、秩，无分布检验（亦称自由分布检验）与关联的区间估计和点估计，符号 检验，符号秩检验，秩检验， Mann Whitney Wilcoxon 检验; KolmogorovSmirnov 检验，排列检验法，含有零值和同分值的离散数据的处理方法；计算 技术与编程，与参数方法的比较。 哈佛大学[BIO 243a.] Nonparametric Methods 2.5 credits Not to be given ; offered alternate years. Lectures. Two 2-hour sessions each week. Presents the theory and application of nonparametric methods. Topics include permutation tests, permutation limit theorems, 2-sample rank tests and their asymptotic efficiency, k-sample rank tests, 1-sample tests of location, paired comparisons, rank tests for symmetry and independence, and analogues of linear modeling based on ranks. Course Note: BIO 231cd required.非参数方法：非参数方法的理论和应用。包括排列检验，排列极限理论，两样本 秩检验及其渐进有效性， 个样本的秩检验， k 单样本的位置检验， 配对比较检验， 对称分布和独立分布的秩检验，基于秩的线性模型的模拟。研究生宾夕法尼亚大学915. Nonparametric Inference. (M) Huang. Prerequisite(s): STAT 511 or equivalent.Statistical inference when the functional form of the distribution is not specified. Nonparametric function estimation, density estimation, survival analysis, contingency tables, association, and efficiency. 非参数推断：分布的函数形式未知时的统计推断。非参函数估计，密度估计，生 存分析，列联表，关联和效率。 UC-BerkleyNonparametric and Robust Methods -- Statistics (STAT) 240 [4 units] Standard nonparametric tests and confidence intervals for continuous
nonparametric est robust estimation of location and scale parameters. Efficiency comparison with the classical procedures.非参数和稳健方法：连续和类别数据的标准非参数检验和置信区间，分位数的非参数估计，阶和位置参数的稳健估计，传统方法的效果比较。 UCLA 201. Nonparametric and Robust Statistics. (4) (Formerly numbered Mathematics 278B.) Lecture, three hours. Requisite: course 200B. Development of nonparametric and robust procedures for hypothesis testing, estimation in one- and two-sample problems, linear and nonlinear regression, multiple classification, density estimation. Letter grading. 非参数统计和稳健统计： 非参数假设检验和稳健假设检验的发展， 单样本和双样本问题的估 计，线性和非线性回归，多重分类，密度估计。 204. Nonparametric Function Estimation and Modeling. (4)(Formerly numbered Mathematics 278I.) Lecture, three hours. Requisite: course 200A. Introduction to many useful nonparametric techniques such as nonparametric density estimation, nonparametric regression, and high-dimensional statistical modeling. Some semiparametric techniques and functional data analysis. Letter grading.非参数函数估计和建模：介绍使用的非参数技术，如：非参数密度估计，非参数回归和高维 统计建模。一些半参数技术和泛函数据分析。 密歇根大学 Statistics 561: TOPICS IN NONPARAMETRIC MODELINGPrerequisites: Statistics 510 & 511, or permission. (3) This course introduces computer-intensive nonparametric statistical methodology. Topics covered include: density estimation (incorporating such topics as kernel density estimates, nearest neighbor estimates, variable kernel methods, orthogonal series estimates, maximum penalized likelihood estimates, bandwidth choice), nonparametric regression (incorporating kernel methods, spline methods, penalized likelihood, bandwidth choice, generalized additive models), and bootstrapping.非参数建模专题：基于计算机的非参数统计方法论。包括：密度估计（如：核密度估计，最 近邻估计， 可变核方法， 正交序列估计， 最大补偿似然估计法， 带宽选择） 非参数回归(如： ， 核方法，样条方法, 补偿似然估计，带宽选择，广义可加模型，)和 bootstrapping 方法。 Statistics 660, 661: NON-PARAMETRIC STATISTICS I & IIPrerequisite: Permission. 660, I; 661, II. (3 each) Special topics in non-parametric inference. For example: locally most regression and the analysis of
asymptotic
g permutation tests and randomization as a basis for inference.非参数统计推断：非参数统计推断专题。如：局部最大功效秩检验（；利用秩进 行回归和方差分析；渐进功效和效率 ，拟合优度检验，排列检验和作为推断基 础的随机化。北卡大学开设了以下高级研究生课程：222- NONPARAMETRIC INFERENCE: RANK-BASED METHODS Prerequisites, Statistics 155, 165. Estimation and testing when the functional form of the population distribution is unknown. Rank, sign, and permutation tests. Optimum nonparametric tests and estimators, including simple multivariate problems. Sen. (3).非参数统计推断: 秩方法：当总体分布函数形式未知时的估计和检验， 秩检验、符号检验 和排列检验. 包含简单多元问题的最优参数检验和估计。 223- NONPARAMETRIC INFERENCE: SMOOTHING METHODS Prerequisites, Statistics 155, 165. Density and regression estimation when no parametric model is assumed. Kernel, spline, and orthogonal series methods. Emphasis on analysis of the smoothing problem and data based smoothing parameter selectors. Marron. (3). 非参数统计推断: 平滑方法：无假定的参数模型时的密度和回归估计。核，样条, 和正交序 列方法。强调平滑问题和基于数据的平滑参数选择。262- NONPARAMETRIC MULTIVARIATE ANALYSIS Prerequisite, Statistics 222. Nonparametric MANOVA. Large sample properties of the tests and estimates. Robust procedures in general linear models including the growth curves. Nonparametric classification problems. Sen. (3).非参数多元分析：检验和估计的大样本性质。 包括成长曲线的广义线性模型的稳 健估计方法。非参数分类问题。 查询稳健估计研究生（Robust estimation）芝加哥大学 369. Robust EstimationThe theory of M-estimators and linear functions history of robust inference, rejection of outliers, influence functions.稳健估计：M 估计量（M-estimators）理论与顺序统计量的线性函数，稳健推断的历史，剔 除异常值，影响函数（influence functions） 。 此外，很少有单独的一门课，一般在统计推断、回归分析、非参数统计、统计计算、线性模 型、时间序列、多元统计、统计讨论课包含等课程中都有涉及。 查询回归分析与方差分析（Regression）方差分析：很少单独开课，除与回归同开外，在实验设计、多元分析、抽样调查中均有涉及。本科威斯康星大学 333 Applied Regression Analysis. I, II, SS; 3 cr (r-N-A). An introduction to regression with emphasis on the practical rather than the theoretical aspects. Begins with fitting a straight line, converts this problem into matrix terms and then proceeds to fitting and evaluation of general linear models. P: Cons inst.应用回归分析：强调实践而不是理论，从拟合直线开始，进而将问题用矩阵形式表示，再进 行拟合和评价。 UCLA M120A-M120B. Regression Analysis. (4-4) (Formerly numbered M153A-M153B.) (Same as Biomathematics M153A-M153B and Biostatistics M153A-M153B.) Lecture, discussion, one hour. Requisites: course 100B, Mathematics 115A. Linear and nonlinear regression analysis using package programs. Emphasis on relation between statistical theory, numerical results, and analysis of data. P/NP or letter grading. M120A. BMDP, SAS, and SPSS general linear transf re model building. M120B. Analysis of var nonlinear regression programs, analysis, maximum robust regression.回归分析：运用软件包进行线性和非线性回归。注重统计理论，数值结果和数据分析。使用 BMDP, SAS 和 SPSS 回归程序，广义线性模型理论，线性回归， 变换和}