Statistics Department
Course List for Fall 2013/Spring 2014

• Organizational Meeting for all FALL term courses whose times are not listed below: August 27, 2013 at 10:00am in Rm 107 (tentative).
• Those interested in attending one of the courses but unable to be present at this meeting should inform Jay Emerson beforehand and submit their schedules.
• Courses numbered 600 or above (such as Stat 610a) are intended primarily for graduate students. If such a course does not have an undergraduate cross-listing, undergraduates need special permission to enroll.
• Tables of class times: FALL and SPRING
• Course lists for other years:
     1994-95    1995-96    1996-97    1997-98    1998-99    1999-00    2000-01    2001-02    2002-03    2003-04    2004-05    2005-06    2006-07    2007-08    2008-09    2009-10    2010-11    2011-12    2012-13    [this year]    2014-15    2015-16    2016-17    2017-18    2018-19    2019-20    2020-21    2021-22    2022-23    2023-24    2024-25 .
  These pages are not being updated; some of the links may no longer be working.
• Director of Undergraduate Studies: Andrew Barron
• Director of Graduate Studies: Jay Emerson and David Pollard
• Course requirements for Ph.D. and M.A. students.

Introductory Statistics (STAT 100b/STAT 500b)

Instructor: Joe Chang
Time: Mon, Wed, Fri 10:30-11:20
Place: ML 211
An introduction to statistical reasoning. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Application of statistical concepts to data; analysis of real-world problems.

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Introduction to Statistics (STAT 101a-106a/STAT 501a-506a)

Instructor: Jonathan Reuning-Scherer and Staff
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Webpage:  http://www.stat.yale.edu/Courses/QR/stat101106.html
A basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks of classes are attended by all students in STAT 101-106 together, as general concepts and methods of statistics are developed. The remaining weeks are divided into field-specific sections that develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence and only one may be taken for credit. No prerequisites beyond high school algebra. May not be taken after STAT 100 or 109.

Students enrolled in STAT 101-106 who wish to change to STAT 109, or those enrolled in STAT 109 who wish to change to STAT 101-106, must submit a course change notice, signed by the instructor, to their residential college dean by Friday, September 28. The approval of the Committee on Honors and Academic Standing is not required.

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Introduction to Statistics: Life Sciences (STAT 101a/E&EB 210aG/MCDB 215a)

Instructor: Jonathan Reuning-Scherer and Walter Jetz
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical and probabilistic analysis of biological problems presented with a unified foundation in basic statistical theory. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics.

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Introduction to Statistics: Political Science (STAT 102a/EP&E 203a/PLSC 425a)

Instructor: Jonathan Reuning-Scherer and Alan Gerber
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical analysis of politics and quantitative assessments of public policies. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and health policy.

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Introduction to Statistics: Social Sciences (STAT 103a/SOCY 119a)

Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Descriptive and inferential statistics applied to analysis of data from the social sciences. Introduction of concepts and skills for understanding and conducting quantitative research.

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Introduction to Statistics: Medicine (STAT 105a)

Instructor: Jonathan Reuning-Scherer and David Salsburg
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical methods used in medicine and medical research. Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data.

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[ Introduction to Statistics: Data Analysis (STAT 106a) ]

Introduction to Statistics: Fundamentals (STAT 109a)

Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
General concepts and methods in statistics. Meets for the first half of the term only. May not be taken after STAT 100 or 101-106.

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Introductory Data Analysis (STAT 230b/STAT 530b/PLSC 530b)

Instructor: John Emerson
Time: Tues, Thurs 9:00-10:15
Place: Mason 211 (with occasional use of TEAL)
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used. After STAT 100 or the equivalent or with permission from the instructor.

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Probability and Statistics (STAT 238a/STAT 538a)

Instructor: Joseph Chang and Lisha Chen
Time: Tues, Thurs 1:00-2:15
Place: ML 211
Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. Statistical inference with emphasis on the Bayesian approach: parameter estimation, likelihood, prior and posterior distributions, Bayesian inference using Markov chain Monte Carlo. Introduction to regression and linear models. Computers are used for calculations, simulations, and analysis of data.

After MATH 118 or 120. Some acquaintance with matrix algebra and computing assumed.
Extra: STAT 238 Extra Session,  Tues 6:30-8:00,  24 Hillhouse Rm 107

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Probability Theory with Applications (STAT 241a/STAT 541a/MATH 241a)

Instructor: Harrison Zhou
Time: Mon, Wed, Fri 9:25-10:15
Place: Loria 250
Introduction to probability theory. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling.
Extra: STAT 241 TA Session,  Thurs 6:30-7:30,  24 Hillhouse Rm 107

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Theory of Statistics (STAT 242b/542b)

Instructor: Lisha Chen
Time: Mon, Wed, Fri 9:25-10:15
Place: ML 211
Study of the principles of statistical analysis. Topics include maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Some statistical computing.

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Stochastic Processes (STAT 251b/STAT 551b)

Instructor: Sahand Negahban
Time:  Mon, Wed 1:00-2:15
Place: WLH 119
Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion, and diffusions. Techniques in probability, such as coupling and large deviations. Applications chosen from image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution.

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Linear Models (STAT 312a/STAT 612a)

Instructor: Joseph Chang
Time: Mon, Wed 11:35-12:50
Place: 17 Hillhouse Rm 05
The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms, with particular reference to the R statistical language.

After STAT 242 and MATH 222 or 225.

No final exam.

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Advanced Probability (STAT 330b/STAT 600b/MATH 330b)

Instructor: David Pollard
Time: Tues, Thurs 2:30-3:45
Place: 24 Hillhouse Rm 107
Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed.

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Data Analysis (STAT 361a/STAT 661a)

Instructor: Lisha Chen
Time: Mon, Wed 2:30-3:45
Place: DL 220
Selected topics in statistics explored through analysis of data sets using the R statistical computing language. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering. Weekly sessions in the Statistical Computing laboratory.

After STAT 242 and MATH 222 or 225, or equivalents.

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Multivariate Statistics for Social Sciences (STAT 363b/STAT 660b)

Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: KRN 301
Introduction to the analysis of multivariate data as applied to examples from the social sciences. Topics include principal components analysis, factor analysis, cluster analysis (hierarchical clustering, k-means), discriminant analysis, multidimensional scaling, and structural equations modeling. Extensive computer work using either SAS or SPSS programming software.

Prerequisites: knowledge of basic inferential procedures and experience with linear models.

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Information theory (STAT 364b/STAT 664b)

Instructor: Andrew Barron
Time: Tues, Thurs 4:00-5:15
Place: 24 Hillhouse Rm 107
Foundations of information theory in mathematical communications, statistical inference, statistical mechanics, probability, and algorithmic complexity. Quantities of information and their properties: entropy, conditional entropy, divergence, redundancy, mutual information, channel capacity. Basic theorems of data compression, data summarization, and channel coding. Applications in statistics and finance. After Statistics 241.

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[ Data Mining and Machine Learning (STAT 365b/STAT 665b) ]

Individual Studies (STAT 480ab)

Instructor: Staff
Time: -
Place: -
Directed individual study for qualified students who wish to investigate an area of statistics not covered in regular courses. A student must be sponsored by a faculty member who sets the requirements and meets regularly with the student. Enrollment requires a written plan of study approved by the faculty adviser and the director of undergraduate studies.

Permission required. No final Exam.

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Senior Seminar and Project (STAT 490b)

Instructor: Andrew Barron
Time: Monday 6:00-8:00
Place: 24 Hillhouse Rm 107
Under the supervision of a member of the faculty, each student works on an independent project. Students participate in seminar meetings at which they speak on the progress of their projects.

Permission required. No final Exam.

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[ Empirical Processes (STAT 609b) ]

Statistical Inference (STAT 610a)

Instructor: David Pollard
Time: Tues, Thurs 10:30-11:45
Place: 24 Hillhouse Rm 107
A systematic development of the mathematical theory of statistical inference covering methods of estimation, hypothesis testing, and confidence intervals. An introduction to statistical decision theory. Undergraduate probability at the level of Statistics 241a assumed.
Extra: STAT 610 Extra Session,  Fri 10:30-11:45,  24 Hillhouse Rm 107

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[ Decision Theory (STAT 611b) ]

[ Experimental Design (STAT 613b) ]

[ Asymptotics (STAT 618a) ]

Statistical Case Studies (STAT 625a)

Instructor: John Emerson
Time: Tues, Thurs 9:00-10:15
Place: 17 Hillhouse Rm 111
Webpage:  https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R.

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Practical Work (STAT 626b)

Instructor: Jay Emerson
Time: -
Place: -
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician. This course is a one-credit requirement for the Ph.D. degree.

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Statistical Consulting (STAT 627ab)

Instructor: John Emerson
Time: Friday 2:30-4:30
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~jay/627.html
Statistical consulting and collaborative research projects often require statisticians to explore new topics outside their area of expertise. This course exposes students to real problems, requiring them to draw on their expertise in probability, statistics, and data analysis. Students complete the course with individual projects supervised jointly by faculty outside the department and by one of the instructors. Students enroll for both terms and receive one credit at the end of the year.

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Statistical Methods in Genetics and Bioinformatics (STAT 645b)

Instructor: Jing Zhang
Time: Wednesday 4:00-6:30
Place: 24 Hillhouse Rm 107
Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics; stochastic modeling and statistical methods applied to problems such as mapping disease-associated genes, analyzing gene expression microarray data, sequence alignment, and SNP analysis. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and some methods of classification and clustering; models include hidden Markov models, Bayesian networks, and the coalescent. The limitations of current models, and the future opportunities for model building, are critically addressed. Prerequisite: STAT 661a, 538a, or 542b. Prior knowledge of biology is not required, but some interest in the subject and a willingness to carry out calculations using R is assumed.

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Topics in Bayesian Inference and Data Analysis (STAT 654a)

Instructor: Jing Zhang
Time: Mon, Wed 10:30-11:45
Place: 24 Hillhouse Rm 107
Topics in the theory and practice of Bayesian statistical inference, ranging from a review of fundamentals to questions of current research interest. Motivation for the Bayesian approach. Bayesian computation, Monte Carlo methods, asymptotics. Model checking and comparison. A selection of examples and issues in modeling and data analysis. Discussion of advantages and difficulties of the Bayesian approach. After STAT 242, 251, and 661 or the equivalent.

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[ Statistical Computing (STAT 662a) ]

[ Probabilistic Networks, Algorithms, and Applications (STAT 667a) ]

Applied Spatial Statistics (STAT 674b/F&ES 781b)

Instructor: Timothy Gregoire and Jonathan Reuning-Scherer
Time: Tues, Thurs 10:30-11:50
Place: KRN 319
An introduction to spatial statistical techniques with computer applications. Topics include spatial sampling, visualizing spatial data, quantifying spatial association and autocorrelation, interpolation methods, fitting variograms, kriging, and related modeling techniques for spatially correlated data. Examples are drawn from ecology, sociology, public health, and subjects proposed by students. Four to five lab/homework assignments and a final project. The class makes extensive use of the R programming language as well as ArcGIS.

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High-Dimensional Statistical Estimation (STAT 679a)

Instructor: Sahand Negahban
Time: Tues, Thurs 12:00-1:15
Place: 24 Hillhouse Rm 107
In this course we will review the recent advances in high-dimensional statistics. We will cover concepts in empirical process theory, concentration of measure, and random matrix theory in the context of understanding the statistical properties of high-dimensional estimation methods. In this discussion we will also overview the computational constraints that are involved with solving high-dimensional problems and touch upon concepts in convex optimization and online learning.

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Nonparametric Statistics (STAT 680a)

Instructor: Harrison Zhou
Time: Tues 4:00-6:30
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~hz68/680/
Introduction to nonparametric methods such as kernel estimation, Fourier basis estimation, wavelet estimation. Optimal minimax convergence rates and constants for function spaces, with connections to information theory. Adpative estimators (e.g., adaptive shrinkage estimation). If time permits: high dimensional function estimation, functional data estimation, classification, or nonparametric asymptotic equivalence. Applications to real data. Some knowledge of statistical theory at the level of STAT 610a is assumed.
Extra: Informal discussion session,  Tuesday 18:00-21:00,  24 Hillhouse Room 107

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Nonparametric Estimation and Statistical Learning (STAT 681b)

Instructor: Alexandre Tsybakov
Time: Tues, Thurs 10:30-11:45
Place: 24 Hillhouse Rm 107
The aim of this course is to give a mathematical introduction to Nonparametric Estimation and Statistical Learning Theory. These two fields that have been developed in parallel by different communities will be presented from a unified point of view, with the emphasis on the construction of optimal methods. The concepts of minimax optimality, adaptivity, as well as the oracle approach will occupy a central place in these developments. The general framework presented in the course allows one to treat simultaneously and by the same techniques two important problems that have been intensively studied during the last decade: Aggregation of arbitrary estimators, and Estimation in high-dimensional linear model under the sparsity scenario.

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Independent Study or Topics Course (STAT 690ab)

Instructor: David Pollard
Time: -
Place: -
By arrangement with faculty. Approval of Director of Graduate Studies required.

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Research Seminar in Probability (STAT 699ab)

Instructor: Sekhar Tatikonda and David Pollard
Time: Friday 11:00-1:00
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~ypng
Continuation of the Yale Probability Group Seminar. Student and faculty explanations of current research in areas such as random graph theory, spectral graph theory, Markov chains on graphs, and the objective method.

Credit only with the explicit permission of the seminar organizers.

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Departmental Seminar (STAT 700ab)

Instructor: -
Time: Monday 4:15-5:30
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/Seminars/2011-12/
Important activity for all members of the department. See webpage for weekly seminar announcements.

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