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 THEORY
Prerequisites, 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 chains on 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: 0826
David van Dyk 2669
Half course (fall term). Th., 2–4.
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.
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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.

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); robustness; 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 theory are discussed; 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 2000–01.
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: randomized experiments; sequential randomization; partial compliance; 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.
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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: basic tools; counting methods; order statistics, ranks; distribution free tests and associated interval and point estimators; sign test; signed rank tests; rank tests; Mann Whitney Wilcoxon procedures; Kolmogorov Smirnov tests; permutation methods; methods for discrete data with zeros and ties; computer techniques and programs; 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 2000-2001; 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,k个样本的秩检验,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-Berkley
Nonparametric and Robust Methods -- Statistics (STAT)240[4units]
Standard nonparametric tests and confidence intervals for continuous and categorical data; nonparametric estimation of quantiles; 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 MODELING
Prerequisites: 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 & II
Prerequisite: Permission. 660, I; 661, II. (3 each)
Special topics in non-parametric inference. For example: locally most powerful rank tests; regression and the analysis of variance using ranks; asymptotic power and efficiency; goodness of fit tests; 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.
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稳健估计 (Robust estimation)
研究生
芝加哥大学
369. Robust Estimation
The theory of M-estimators and linear functions of order statistics; 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.
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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, three hours; 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 regression programs; general linear model theory; linear regression analysis; transforming and weighting; regression diagnostics; model building. M120B. Analysis of variance and covariance; nonlinear regression programs, analysis, and applications; maximum likelihood analysis; 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, 变换和加权, 回归诊断,建模;方差和协方差分析,非线性回归程序,分析和应用,极大似然分析,robust 回归.
芝加哥大学:
224. Applied Regression Analysis. PQ: Stat 220 or equivalent. This course is an introduction to the methods and applications of fitting and interpreting multiple regression models. The primary emphasis is on the method of least squares and its many varieties. Topics include the examination of residuals, the transformation of data, strategies and criteria for the selection of a regression equation, the use of dummy variables, tests of fit, nonlinear models, biases due to excluded variables and measurement error, and the use and interpretation of computer package regression programs. The techniques discussed are illustrated by many real examples involving data from both the physical and social sciences. Matrix notation is introduced as needed. Staff. Autumn.
应用回归分析:多元回归模型的拟合与解释方法与应用介绍.注重最小二乘方法.包括:参差检验,数据变换,回归方程选择的策略和条件,哑变量的运用,拟合检验,非线性模型,测量误差和剔除变量产生的偏差,回归软件包的运用和解释.运用许多物理学和社会学的实例.根据需要介绍矩阵定义.
哈佛大学
Statistics 139. Regression Analysis
An introduction to data analysis using multiple regression. Topics may include model building and diagnostics, graphical checks of assumptions, transformations, multivariate graphics and visualization, exploratory data analysis, tests of significance and confidence intervals, and logistic regression. The course will emphasize analysis and investigation of real datasets using computer software.
Prerequisite: Statistics 100 or equivalent.
回归分析:模型建立和诊断,前提假定的图形验证(graphical checks of assumptions),多变量图形与目测,探索性数据分析,显著性检验,logistic回归.强调运用计算机软件进行真实数据的分析.
Statistics 239. Advanced Regression Analysis
Besides the applications done jointly with Statistics 139, students meet separately to develop the theory (multivariate normal, maximum likelihood, likelihood ratio tests, Gauss-Markov, etc.) of linear models. Students do some of the homework assignments from Statistics 139, but also other assignments that differ and are more advanced. Grading is separate from Statistics 139.
Prerequisite: Probability and statistics at the level of Statistics 110 and 111.
高级回归分析:学生需要独立的深入学习线性模型理论(多维正态,极大似然,似然比率检验,高斯-马尔可夫理论等). 学生需要做一些更深的作业.
BIO 235cd. Regression and Analysis of Variance 5 credits
Lectures, laboratories. Two 2-hour sessions each week. One 2-hour lab each week.
This is an advanced course in data analysis for linear models - regression and analysis of variance. Estimation methods (maximum likelihood and least squares) and issues of inference (confidence intervals, hypothesis testing, analysis of residuals) are presented from a theoretical and data analysis perspective.
Course Note: BIO 232ab and BIO 231cd, or signature of instructor required; familiarity with matrix algebra and BIO 211cd or equivalent recommended. Lab or section time to be announced at first meeting.
回归分析与方差分析:线性模型数据分析的高级课程.理论结合实际数据分析介绍估计方法(极大似然分析和最小二乘分析)和推断问题(置信区间,假设检验和残差分析).

BIO 213ab. Applied Regression for Clinical Research 5 credits
Lectures. Two 1.5-hour sessions each week. One 1.5-hour lab each week.
This course will introduce students involved with clinical research to the practical application of multiple regression analysis. Linear regression, logistic regression and proportional hazards survival models will be covered, as well as general concepts in model selection, goodness-of-fit, and testing procedures. Each lecture will be accompanied by a data analysis using SAS and a classroom discussion of the results. The course will introduce, but will not attempt to develop the underlying likelihood theory.
Course Note: Previous introductory level statistics course and SAS programming ability required.
临床研究中的回归应用:模型选择的一般思想,线性回归,logistic 回归,比例风险生存模型(proportional hazards survival models )拟合优度,检验步骤.课程将与使用SAS 进行数据分析并进行课堂讨论.
[BIO 217t]. Linear Regression and Longitudinal Analysis 2.5 credits
Not to be given 2000-2001; offered alternate years.
Lectures. Five 2-hour sessions each week.
An introductory course in multiple linear regression and linear models for longitudinal data. The course will begin with the concepts and statistical principles underlying linear regression analysis, then describe methods for multiple regression analysis, and conclude with an introduction to the use of linear models in the analysis of longitudinal data. Homework assignments will focus on the use of both STATA and SAS software, especially the GLM and MIXED procedures in SAS, to develop proficiency in regression analysis.
Course Note: BIO 200ab, BIO 201ab, BIO 206s, BIO 207t, BIO 219ab, BIO 200s and BIO 200t, or another semester-long introductory course in biostatistics required.
线性回归与纵向分析:纵向数据的多元线性回归和线性模型介绍.从线性回归统计思想,统计原则开始,进而描述多元回归方法,引出纵向数据的线性模型.作业使用STATA 和 SAS 软件,特别是SAS 中的GLM和 MIXED程式.
[BIO 278a.] Regression with Non-Normal Data 2.5 credits
Not to be given 2000-2001; offered alternate years.
Lectures. Two 2-hour sessions each week.
This course is intended as an advanced course in statistical models for non-normal data. Generalized linear models will be introduced as a unifying framework for commonly used statistical methods such as multiple regression, logistic regression and Poisson regression. Likelihood inference will be developed using the exponential family of distributions. Extensions to quasi-likelihood methods will also be discussed, which allow the relaxation of some model assumptions. Finally, nonparametric models will be introduced in which the "linear" assumptions of the generalized linear models can be relaxed. In this context, we will explore the use of generalized additive models in fitting nonparametric models to non-normal data.
非常规数据的回归:该课为非正规数据的统计方法高级课程.介绍作为统计方法如:多元回归,logistic回归和泊松回归的共同框架的广义线性模型.运用指数族分布发展(develope)似然推断,还将讨论允许放松一些模型假设的准似然方法的扩展.在`放松广义线性模型的线性假设时引出非参数模型.在此背景下,探索拟和非正常数据的非参数模型中广义附加模型的运用.
研究生
宾夕法尼亚大学沃顿商学院
500. (PSYC611) Applied Regression and Analysis of Variance. (A) Staff. Prerequisite(s): STAT 112 or equivalent.
An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences.
应用回归和方差分析:多元回归和方差分析的研究生课程.强调数据分析和解释的实际方法.包括建模,广义线性假设,残差分析,杠杆效应与影响(leverage and influence),方差单向分析,方差双向分析, 因子分析.管理学,行为科学,社会学和健康科学博士生基础课.
华盛顿大学
STAT 423 Applied Regression and Analysis of Variance (4) NW
Regression analysis. Problems in interpreting regression coefficients. Estimation, including two-stage least squares. Guided regression: building linear models, selecting carriers. Regression residuals. Analysis of variance. Nonparametric regression. Factorial designs, response surface methods. Prerequisite: either STAT 342, STAT/MATH 390, STAT 421, or STAT/ECON 481; recommended: MATH 308. Offered: W.
应用回归分析与方差分析:如何解释回归系数,估计(含两阶段最小二乘),建模,选择变量,,回归残差;方差分析;非参数回归;析因设计, 反应曲面法.
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随机过程(Stochastic Processes)
本科
哈佛大学
Statistics 171. Introduction to Stochastic Processes
An introductory course in stochastic processes. Topics include Markov chains, branching processes, Poisson processes, birth and death processes, renewal theory, queuing theory, Brownian motion, and Martingales.
Prerequisite: Statistics 110 or equivalent.
随机过程引论:随机过程结束.包括:马尔可夫链,分支过程,泊松过程,生灭过程,更新理论,排队论,布朗运动和鞅.
Statistics 271. Stochastic Processes in Continuous Time
Modeling and statistical analysis for Gaussian processes governed by stochastic differential equations with applications to control engineering and financial modeling.
连续时间上的随机过程:控制工程和金融模型的随机微分方程下的高斯过程建模和统计分析.
华盛顿大学
STAT 491 Introduction to Stochastic Processes (3) NW
Random walks, Markov chains, branching processes, Poisson process, point processes, birth and death processes, queuing theory, stationary processes. Prerequisite: 2.0 in MATH/STAT 396. Offered: jointly with MATH 491; A.
随机过程介绍:随机游走,马尔可夫链,分支过程,泊松过程,点过程,生灭过程,排队论,平稳过程.
康奈尔大学
STENGR 361 Introductory Engineering Stochastic Processes I (Enroll in OR&IE 361.) 4 credits. Lecs: TR 10;10-11:25. Secs: MW 12:20-2:15, T 2:30-4:25, W 12:20-2:15. Prerequisite: OR&IE 360 or equivalent. Instructor: P. Protter. Basic concepts and techniques of random processes are used to construct models for a variety of problems of practical interest. Topics include the Poisson process, Markov chains, renewal theory, models for queuing and reliability.
工程随机过程介绍:各种现实问题的模型构建中的随机过程的基本概念和技术.泊松过程,马尔可夫脸,更新理论,排队模型和可靠性.
UC-Berkley
Stochastic Processes -- Statistics (STAT) 150 [3 units]
Description: Random walks, discrete time Markov chains, Poisson processes. Further topics such as: continuous time Markov chains, queueing theory, point processes, branching processes, renewal theory, stationary processes, Gaussian processes.
随机过程:随机游走,离散时间的马尔可夫链, 泊松过程,更深的内容:连续时间的马尔可夫链,排队论,点过程,分支过程,更新理论,平稳过程,高斯过程.
研究生
UC-Berkley
Stochastic Processes -- Statistics (STAT) 206A [3units]
Description: The content of this course changes from year to year. Course topics will be selected from: the general theory of processes, sample function properties, weak convergence, Brownian motion, diffusions, Levy processes, Markov processes, martingales, Gaussian processes and further topics.
随机过程:课程的内容每年有所变化.课程论题将从以下选择:过程的一般理论,样本函数的特性,弱收敛,布朗运动,扩散,Levy过程,马氏过程,鞅,高斯过程及更深的论题.
Applied Stochastic Processes -- Statistics (STAT) 250 [3 units]
Description: Various aspects of applied stochastic processes. Offered according to student demand and faculty availability.
应用随机过程:随机过程的应用:根据学生的需要和教师所能提供的内容确定.
威斯康星大学
632 Introduction to Stochastic Processes. (Crosslisted with Math, Ind Engr, O I M) I, SS; 3 cr (N-A). Markov chains: classification, recurrence, transcience, limit theory. Renewal theory, Markov processes, birth-death processes. Applications to queueing, branching, and other models in science, engineering and business. Topics drawn from semi-Markov processes, martingales, Brownian motion. P: Math 431, or Stat 309
随机过程介绍:马尔可夫链:分类,常返状态,滑过状态,极限理论.更新理论,马尔可夫过程,生灭过程.排队过程,分枝过程及其他模型及其在自然科学,工程学和经济学中的应用.准马尔可夫过程,样,布朗运动.
宾夕法尼亚大学沃顿商学院
901. (OPIM931) Stochastic Processes II. (M) Staff. Prerequisite(s): OPIM 930 or equivalent.
Martingales, optimal stopping, Wald's lemma, age-dependent branching processes, stochastic integration, Ito's lemma.
随机过程:鞅,最优停步,Wald引理,依赖年龄的分支过程,随机积分, Ito引理.

滑铁卢大学 本科,研究生
STAT 833 Stochastic Processes I F (0.5)
Random walks, renewal theory and processes and their application, Markov chains, branching processes, statistical inference for Markov chains.
随机过程:随机游走,更新理论和过程及其应用,马尔可夫链,分支过程,马尔可夫链统计推断.
北卡大学 高级研究生课程
232- STOCHASTIC PROCESSES
Prerequisites, Statistics 154 and 155. Advanced theoretic course including topics selected from: Foundations of stochastic processes, renewal processes, stationary processes, Markov processes, martingales, point processes. Kallianpur. (3).
随机过程:高级理论课程,包括:随机过程基础,更新过程,平稳过程,马尔可夫过程.
235- POINT PROCESSES
Prerequisite, Statistics 155. Random measures and point processes on general spaces, general Poisson and related processes, regularity, compounding. Point processes on the real line, stationarity and Palm distributions, Palm-Khintchine formulae. Convergence of point processes and related topics. Leadbetter. (3).
点过程: 一般空间的随机测度和点过程,一般泊松过程和相关过程,规则化,复合.实值点过程,平稳性和Palm 分布,Palm-Khintchine公式.点过程的收敛及相关话题.
236- STOCHASTIC ANALYSIS
Prerequisite, Statistics 154 amd 155, or permission of the instructor. Advanced course covering topics selected from: semimartingale theory, stochastic integrals, homogeneous chaos expansions, stochastic differential equations, Malliavin calculus, infinite dimensional processes, functional central limit theorems, Feynman-Kac formula, Feynman integral. Applications to filtering theory, infinite particle systems, quantum mechanics, and stochastic models in neurophysiology. Kallianpur. (3).
随机分析:高级课程,包括: 半鞅理论, 随机积分,齐次混沌展开,随机差分方程, Malliavin 微积分, ,有限维过程,泛函中心极限理论, Feynman-Kac公式, Feynman积分, 过滤理论的应用,无限粒子系统,量子力学,神经学中的随机模型.
密歇根大学
Statistics 526 (Math 526): DISCRETE STATE STOCHASTIC PROCESSES
Prerequisite: Statistics or Mathematics 525, or EECS 501, or permission. I, II. (3)
Generating functions; recurrent events and the renewal theorem; random walks, Markov chains; branching processes; limit theorems; Markov chains in continuous time with emphasis on birth and death processes and queueing theory. An introduction to Brownian motion, stationary processes, and martingales.
离散状态随机过程:生成函数, 常返事件和更新理论,随机游走,马氏链,分支过程,极限理论,强调生灭过程和排队论的连续时间的马氏链,布朗运动介绍,平稳过程,鞅.
芝加哥大学
312. Introduction to Stochastic Processes I. PQ: Stat 251, and Math 201 or 204. This course is an introduction to stochastic processes not requiring measure theory. Topics include branching processes, recurrent events, renewal theory, random walks, Markov chains, Poisson, and birth-and-death processes. Staff. Winter.
随机过程介绍:无需测度论知识的随机过程介绍.包括:分枝过程,常返事件,更新理论,随机游走,马尔可夫链,泊松过程和生灭过程.
313. Introduction to Stochastic Processes II. PQ: Stat 312 or consent of instructor. This course is a sequel to Stat 312. Topics covered include continuous time Markov chains: birth and death processes and queues, introduction to discrete time martingales, Brownian motion and diffusions. Time permitting: stochastic ordering, Poisson approximations. The emphasis is on defining the processes and calculating or approximating various related probabilities. The measure theoretic aspects of these processes is not covered rigorously. Staff. Spring.
随机过程简介II:包括:连续时间的马尔可夫链:生灭过程和排队过程,离散时间上的鞅,布朗运动和散射.时间允许的话,还包括随机排序,泊松近似.强调过程的定义以及相关概率的计算和近似.不包含测度论内容.
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时间序列分析(Time Series Analysis)
本科
威斯康星大学
349 Introduction to Time Series. II; 3 cr (N-A). Autocorrelation, elements of spectral analysis; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications in the social sciences and environmental studies. P: Stat 301 or equiv, or cons inst.
时间序列引论:自相关,谱分析初步;动态模型;自回归和移动平均模型;识别和拟合;预测;季节调整;社会科学和环境研究中的应用.
UC-Berkley
Introduction to Time Series -- Statistics (STAT) 153 [4 units]
Description: An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.
时间序列引论:介绍时域和谱域的时间序列分析.包括:趋势和季节效应估计,自回归移动平均模型,预报,调和分析(harmonic analysis),谱.
哈佛大学
[Statistics 231. Bayesian Time Series]
A study of the dynamic linear model. Topics may include review of classical time series models, forecasting, smoothing, regression methods, polynomial trend models, seasonal models, and forecast monitoring and intervention. Theory and computational methods will be developed with an emphasis on applications to a variety of data sets.
Note: Expected to be given in 2000-01.
贝叶斯时间序列分析:动态线性模型研究.包括传统的时间序列模型,预测,平滑,回归方法,多项式趋势模型,季节模型,预测监控与干预.通过应用于数据集,发展计算理论与方法.
滑铁卢大学
STAT 443 F,W 3C 0.5
Forecasting
Model building. Multiple regression and forecasting. Exponential smoothing. Box-Jenkins models. Smoothing of seasonal data.
Prereq: STAT 331 or STAT 361 or consent of instructor
预测:建模.多元回归和预测.指数平滑.Box-Jenkins模型.季节数据的平滑.)
研究生
UC-Berkley
Analysis of Time Series -- Statistics (STAT) 248 [4 units]
Description: Frequency-based techniques of time series analysis, spectral theory, linear filters, estimation of spectra, estimation of transfer functions, design, system identification, vector-valued stationary processes, model building.
时间序列分析 :频率域的时间序列分析技术,谱理论,线性过滤法,谱估计,传递函数估计,设计,系统识别,向量值平稳过程(vector-valued stationary processes),建模.
宾夕法尼亚大学沃顿商学院
910. Forecasting and Time Series Analysis. (J) Staff. Prerequisite(s): STAT 511 or 541 or equivalent.
Fourier analysis of data, stationary time series, properties of autoregressive moving average models and estimation of their parameters, spectral analysis, forecasting. Discussion of applications to problems in economics, engineering, physical science, and life science.
预测与时间序列:数据的傅里叶分析,静态时间序列,ARMA模型的性质及其参数估计,谱分析,预测,;在经济,工程,物理和生命科学中的应用.)
密歇根大学
Statistics 531: STATISTICAL ANALYSIS OF TIME SERIES
Prerequisite: Statistics 511, or permission. I. (3)
Decomposition of series; trend and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectogram, periodogram, etc., stochastic difference equations, autoregressive schemes, moving averages; large sample inference and predictions; covariance structure and spectral densities; hypothesis testing and estimation; applications and other topics.
时间序列统计分析:序列的分解,作为时间序列特例的趋势与回归,循环组成,平滑技术,变差法; 谱图,周期图等方法展示,随机微分方程, 自回归方法,移动平均,大样本推断和预测,协方差结构和谱密度,假设检验和估计.
北卡大学
185- TIME SERIES AND MULTIVARIATE ANALYSIS
Prerequisite, Statistics 126. Time Series: Exploratory and graphical analysis; Time domain analysis and ARMA models; Fourier analysis: FFT, periodogram, smoothing; State space analysis: Kalman filter, dynamic models. Multivariate: Principal components, canonical correlation; Classification, clustering; Dimension reduction: projection pursuit, alternating conditional sliced inverse regression. Spring. Leadbetter, Simons. (3)
时间序列和多元分析: 时间序列:探索和图形分析;时域分析和ARMA 模型; 傅里叶分析: FFT, 周期图 平滑;状态空间分析: Kalman过滤法,动态模型.多元分析:主成分分析,典型相关分析;分类,聚类;降维:投影寻踪, 交替条件切片逆回归.
233- TIME SERIES ANALYSIS
Prerequisites, Statistics 185. Analysis of time series data by means of particular models such as autoregressive and moving average schemes. Spectral theory for stationary processes and associated methods for inference. Stationarity testing. Leadbetter. (3).
时间序列分析:运用自回归和移动平均模型进行时间序列分析.平稳过程的谱理论和相关的推断方法.平稳性检验.

不错，辛苦了！

认真学习一下

楼主威武！

Waterloo

感谢分享！

辛苦了！

thanks a lot