Statistics Department
Courselist for Fall 2009/Spring 2010

• Organizational Meeting for all FALL term courses whose times are not listed below: 24 Hillhouse Avenue, Room 107 .
• Those interested in attending one of the courses but unable to be present at this meeting should inform Amy Mulholland beforehand and submit their schedules.
• Courses whose first or only number is 600 or above (such as Stat 600b/330b or 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.
• Schedule of times when 24 Hillhouse Room 107 is in use.
• 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   [this year]    2010-11   2011-12   2012-13   2013-14   2014-15   2015-16   2016-17   2017-18   2018-19   2019-20   2020-21   2021-22   2022-23   2023-24   2024-25   2025-26. These pages are not being updated; some of the links may no longer be working.
• Director of Undergraduate Studies: Andrew Barron (FALL); David Pollard (SPRING)
• Director of Graduate Studies: Andrew Barron (FALL); David Pollard (SPRING)
• Typical 1st-year courses for Ph.D. and M.A. students.

Introductory Statistics (STAT 100b/STAT 500b)

Instructor:  Balaji Raman
Time:  Mon, Wed, Fri 10:30-11:20
Place: 211 Mason Lab
Webpage: 
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 (tentative)
Webpage:  http://www.stat.yale.edu/Courses/QR/stat101106.html
Statistics is the science and art of prediction and explanation. In most fields of study research relies on statistical analysis of data. Each of these courses, led by an expert from the field of study, introduces statistical reasoning and emphasizes how Statistics is applied to the particular discipline. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Students will learn to apply statistical concepts to data using Minitab and reach conclusions about real-world problems. 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 discipline particular to the course (Life Sciences for Stat 101, Political Science for Stat 102, and so on). The courses meet together for the first seven weeks and separately for the final six weeks. The first part of the course is taught by Jonathan Reuning-Scherer and covers fundamentals of probability and statistics. Periodic examples are provided by individual course instructors. The courses separate by area of specialty for the final six weeks.

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

Instructor: Jonathan Reuning-Scherer and Gunter Wagner
Time: Tues, Thurs 1:00 - 2:15
Place: 
Webpage: 
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: 
Webpage: 
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: 
Webpage: 
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: Psychology] (STAT 104a/PSYC 201a)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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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: 
Webpage: 
Statistical methods relied upon 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)

Instructor: Jonathan Reuning -Scherer and Harrison Zhou
Time: Tues, Thurs 1:00 -2:15
Place: TBA
Webpage: 

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Real-World Statistics (STAT 128b)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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

Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 2:30 - 3:45
Place: Statlab, 140 Prospect
Webpage: 
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. Uses SPLUS and Web data sources. After or concurrent with Statistics 101-105.

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Optimization and Convexity (AMTH 237a/AMTH 537a)

Instructor: 
Time: not taught this year
Place: TBA
Webpage: 

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

Instructor: Joseph Chang
Time: Mon, Wed, Fri 2:30-3:20
Place: ML 211
Webpage: 
Fundamental principles and techniques that help scientists think probabilistically, develop statistical models, and analyze data. Essentials of probability: conditional probability, random variables, distributions, law of large numbers, central limit theorem, 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 throughout for calculations, simulations, and analysis of data. After MATH 118a or b or 120a or b. Some acquaintance with matrix algebra and computing assumed.

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

Instructor: Balaji Raman
Time: Mon, Wed, Fri 9:25 - 10:15
Place: WLH 208
Webpage: 
A first course in probability theory: probability spaces, random variables, expectations and probabilities, conditional probability, independence, some discrete and continuous distributions, central limit theorem, law of large numbers. After or concurrent with Mathematics 120a or b or equivalents.

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Theory of Statistics (STAT 542a)

Instructor: Andrew Barron
Time: Mon, Wed, 1:00 - 2:15
Place: 24 Hillhouse, Room 107
Webpage: 
Principles of statistical analysis: maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Intended for Statistics Masters students; others may be admitted with consent of instructor. After or concurrently with Statistics 541a.

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Theory of Statistics (STAT 242b/542b)

Instructor: Mokshay Madiman
Time: Mon, Wed, Fri 9:25 - 10:15
Place: 24 Hillhouse, Room 107
Webpage: 
Principles of statistical analysis: maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. After Statistics 241a; after or concurrent with Mathematics 222.

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Stochastic Processes (STAT 251b/STAT 551b)

Instructor: Balaji Raman
Time:  Mon, Wed 1:00 - 2:15
Place: 24 Hillhouse, Room 107
Webpage: 
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 to image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, genetics and evolution. After Statistics 241a or equivalent.

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Linear Models (STAT 312a/STAT 612a)

Instructor: Balaji Raman
Time: Tues, Thurs 9:00-10:15
Place: 24 Hillhouse, Room 107
Webpage: 
The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms (with particular reference to Splus); alternatives to least squares. Generalized linear models. Linear algebra and some acquaintance with statistics assumed.

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Experimental Design (STAT 313b)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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Advanced Probability (STAT 330b/STAT 600b)

Instructor: David Pollard
Time: Tues, Thurs 2:30 - 3:45
Place: 24 Hillhouse
Webpage:  http://www.stat.yale.edu/~pollard/Courses/600.spring2010/
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: John Emerson
Time: Mon, Wed 2:30 - 3:45
Place: Stat Lab 140 Prospect Street
Webpage: 
Through analysis of data sets using the Splus statistical computing language, study of a selection of statistical topics such as linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification and clustering. After Statistics 242 and Mathematics 222b or 225a or b, 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: 301 Kroon Hall
Webpage: 
A practical introduction to the analysis of multivariate data as applied to examples from the social sciences. Topics to include multivariate analysis of variance (MANOVA), principle components analysis, cluster analysis (hierarchical clustering, k-means), canonical correlation, multidimensional scaling, factor analysis, discriminant analysis, and structural equations modeling. Emphasis is placed on practical application of multivariate techniques to a variety of examples in the social sciences. There are regular homework assignments and a final project. Regular use of some statistical software package (students may choose among SAS, SPSS, and MINITAB). A complete syllabus will be available on the classes server.

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Information theory (STAT 364b/STAT 664b)

Instructor: Mokshay Madiman
Time: Tues, Thurs 9:00 - 10:15
Place: 24 Hillhouse
Webpage: 
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)

Instructor: Harrison Zhou
Time: Mon, Wed 11:35 - 12:50
Place: 24 Hillhouse
Webpage: 
Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.

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Statistics Senior Seminar Project (490b)

Instructor: David Pollard
Time: 
Place: 
Webpage: 
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.

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Applied Math Senior Seminar and Project (AM490a)

Instructor: Andrew Barron
Time: Wed, 3:30 - 5:20
Place: 24 Hillhouse, Room 107
Webpage: 
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. Some meetings are devoted to talks by visiting applied mathematicians.

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Advanced Stochastic Processes (STAT 603b)

Instructor: David Pollard
Time: Mon, Wed 2:30-3:45
Place: 24 Hillhouse, Room 107
Webpage:  http://www.stat.yale.edu/~pollard/Courses/603.spring2010/
Martingales in continuous time, with applications to the stochastic calculus of semimartingales. If time permits, random trees and other exotic random processes will also be discussed.

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Statistical Inference (STAT 610a)

Instructor: Mokshay Madiman
Time: Tues, Thurs 10:30-11:45
Place: 24 Hillhouse, Room 107
Webpage: 
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.

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Asymptotics (STAT 618a)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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Statistical Decision Theory in Modern Statistical Methodology (STAT 619b)

Instructor: 
Time: not taught this year
Place: 
Webpage: 
Shrinkage estimation and its connection to minimaxity, admissibility, Bayes, empirical Bayes, and hierarchical Bayes. Shrinkage captures essential nonlinearity neccessary to outperform standard linear estimators in Gaussian regression models and random effects models. Relationship to model selection and to sparsity in the estimation of functions by selection from large dictionaries of candidate terms. Nonparmetric estimation. Tests of statistical hypotheses. Multiple comparisions. Some knowledge of statistical theory at the level of STAT 610a is assumed.

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Statistical Case Studies (STAT 625a)

Instructor: Jay Emerson
Time: Mon 11:00 - 12:15; Fri 10:30 - 1:00 pm
Place: Stat Lab 140 Prospect Street
Webpage: 
Statistical analysis of a variety of problems which, in past years, have included: the value of a baseball player, the fairness of real estate taxes, how to win the Tour de France, energy consumption in Yale buildings, and interactive questionnaires for course evaluations. We will emphasize methods of choosing data, acquiring data, and assessing data quality. Graduate, professional, and undergraduate students from any department are welcome, but must seek permission (discussing their background in statistics and goals for the semester) at or before the first class meeting. At least one prior course in statistics is required, but the most important prerequisite is a willingness to get your hands dirty working with real data sets. This will entail a certain amount of "programming," which we believe can be best taught by example, trial and error.

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Practical Work (STAT 626b)

Instructor: David Pollard
Time: TBA
Place: 24 Hillhouse
Webpage: 
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician. This course is a one-credit elective requirement for the Ph.D. degree.

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Statistical Consulting (STAT 627ab)

Instructor: Jay Emerson (Fall); John Hartigan (Spring)
Time: Friday 2:30 - 4:30
Place: 24 Hillhouse, Room 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. The course meets once a week all year, and students receive one half-credit each semester.

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Deterministic and Stochastic Optimization (STAT 637a)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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Statistical Methods in Genetics and Bioinformatics (STAT 645b)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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Statistical Computing (STAT 662a)

Instructor: 
Time: not taught this year
Place: 
Webpage: 
Topics in the practice of datat analysis and statistical computing, with particular attention to problems involoving massive data sets or large, complex simulations and computations. Porgamming with R, C/C++, and Perl, memory management, interactive and dynamic graphics, and parallel computing.

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

Instructor: Sekhar Tatikonda
Time: Tues, Thurs 1:00-2:15
Place: 24 Hillhouse, Room 107
Webpage:  http://pantheon.yale.edu/~sct29/courses.dir/prob_networks2009.html
This course examines probabilistic and computational methods for the statistics modeling of complex data. The emphasis is on the unifying framework provided by graphical models, a formalism that merges aspects of graph theory and probability theory. Graphical models: Markov random fields, Bayesian networks, and factor graphs. Algorithms: filtering, smoothing, belief-propagation, sum-product, and juntion tree. Variational techniques: mean-field and convex relaxtions. Markov processes on graphics: MCMC, factored HMMs, and Glauber dynamics. Some statistical physics techniques: cavity and replica methods. Applications to error-correcting codes, computer vision, bio-informatics, and combinatorial optimization.

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Functional Data Analysis (STAT 673a)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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Unsupervised Learning: Dimension Reduction and Clustering Analysis (STAT 675b)

Instructor: 
Time: not taught this year
Place: 
Webpage: 

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Portfolio Estimation for Compounding Wealth (STAT 678b)

Instructor: Andrew Barron
Time: Tues, Thurs 10:30 - 11:45
Place: 24 Hillhouse, Room 107
Webpage: 
Statistical methodology and analysis for compounded wealth in repeated gambling and in stock market investment. Strategies of highest concentrated wealth. Relationship to maximum likelihood and greedy strategies. Universal portfolios and their relationship to Bayes methods. Wealth analysis both for stochastic stock price sequences and its minimax behavior for arbitrary prices sequences. Fast algorithms for universal portfolios. Prerequisite: STAT 542a or 538, or ECON 550a or equivalent.

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Nonparametric Statistics (STAT 680a)

Instructor: Harrison Zhou
Time: Thurs, 3:30 - 5:30
Place: 24 Hillhouse, Room 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 ratesa nd 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.

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Independent Study (STAT 690ab)

Instructor: Staff
Time: -
Place: -
Webpage: 
By arrangement with faculty. Approval of director of graduate studies required.

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Internship in Statistical Research (STAT 695ab)

Instructor: Andrew Barron
Time: -
Place: 
Webpage: 
The Internship is designed to give students an opportunity to gain practical exposure to problems in the analysis of statistical data, as part of a research group within industries such as: medical and pharmaceutical research, financial, information technologies, telecommunications, public policy, and others. The Internship experience often serves as a basis for the Ph.D. dissertation. Students will work with the Director of Graduate Studies and other faculty advisors to select suitable placements, but is distinct from the required Stat 626b. Students will submit a one-page description of their Internship plans to the DGS by May 1st, which will be evaluated by the DGS and other faculty advisors by May 15th. Upon completion of the Internship, students shall submit a written report of their work to the DGS, no later than October 1st. The Internship will be graded on a Satisfactory/ Unsatisfactory basis, and will be based on the student's written report and an oral presentation.

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Research Seminar in Statistics (STAT 699ab)

Instructor: Sekhar Tatikonda and David Pollard
Time: Friday 10:30- 12:30
Place: 24 Hillhouse
Webpage:  http://www.stat.yale.edu/~ypng
Continuation of the Yale Probablistic Networks Group Seminar. Student and faculty expanations of current research in areas such as random graph theory, spectral graph theory, Markov chains on graphs, and the objective method.

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Departmental Seminar (STAT 700ab)

Instructor: -
Time: Monday 4:15 - 5:30
Place: 24 Hillhouse Avenue, room 107
Webpage:  http://www.stat.yale.edu/Seminars/2009-10/
Important activity for all members of the department. See webpage for weekly seminar announcements.

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