Statistics Department
Courselist for Fall 2011/Spring 2012

• Organizational Meeting for all SPRING term courses whose times are not listed below: January 6 2012 at 10:00am in Rm 107 .
• Those interested in attending one of the courses but unable to be present at this meeting should inform Ashley Nesmith . 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.
• Table of class times
• 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    [this year]    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    2026-27 .
  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: David Pollard
• Course requirements for Ph.D. and M.A. students.

Introductory Statistics (STAT 100b/STAT 500b)

Instructor: Andrew Barron
Time: Mon, Wed, Fri 10:30-11:20
Place: Davies Auditorium
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
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.

The first half of the course can be taken for 1/2 credit, as Stat 109a.

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

Instructor: Jonathan Reuning-Scherer and Günter Wagner
Time: Tues, Thurs 1:00-2:15
Place: LOM 206 (Last 6 Weeks)
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 Conor Dowling
Time: Tues, Thurs 1:00-2:15
Place: KBT 1214 (Last 6 Weeks)
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: OLM 202 (Last 6 Weeks)
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: DL 220 (Last 6 Weeks)
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: Harrison Zhou
Time: Tues, Thurs 1:00-2:15
Place: PR 140 STATLAB (Last 6 Weeks)
An introduction to Probability and Statistics with emphasis on data analysis.

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

Instructor: John Emerson
Time: Mon, Wed 2:30-3:45
Place: ML 211
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. Uses R and Web data sources. After or concurrent with Statistics 101-105.

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

Instructor: Jing Zhang
Time: Mon, Wed, Fri 10:30-11:20
Place: BCT CO31
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.
Extra: Problem session,  Wednesday 3:00-5:00,  24 Hillhouse Rm 107

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

Instructor: David Pollard
Time: Mon, Wed, Fri 9:25-10:15
Place: WLH 208
Webpage:  http://www.stat.yale.edu/~pollard/Courses/241.fall2011/
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 242b/542b)

Instructor: Lisha Chen
Time: Mon, Wed, Fri 9:25-10:15
Place: ML 211
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: Jing Zhang
Time:  Mon, Wed 1:00-2:15
Place: LUCE 101
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: Joseph Chang
Time: Mon, Wed 11:35-12:50
Place: 24 Hillhouse Rm 107
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). Linear algebra and some acquaintance with statistics assumed.

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

Instructor: Mokshay Madiman
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: ML 211
Through analysis of data sets using the R 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: Burke Auditorium, KRN 301
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 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)

Instructor: Lisha Chen
Time: Mon, Wed 11:35-12:50
Place: 24 Hillhouse Rm 107
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: Andrew Barron
Time: Wed 3:30-5:30
Place: WLH 209
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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Empirical Processes (STAT 609b)

Instructor: David Pollard
Time: Mon, Wed 1:00-2:15
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~pollard/Courses/609.spring2012/
A rigorous discussion of probabilistic methods distilled from the empirical process literature. Chaining arguments; maximal inequalities; symmetrization and combinatorial entropy; VC dimension and beyond; bracketing; concentration of measure; majorizing measures; uniform laws of large numbers and Donsker theorems. Applications to asymptotics for statistical inference and econometrics. Assumes knowledge of probability theory at the level of Stat 600.

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Statistical Inference (STAT 610a)

Instructor: Andrew Barron
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: Review session,  Friday 1:00-2:20,  24 Hillhouse Rm 107

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Decision Theory (STAT 611b)

Instructor: Harrison Zhou
Time: Wednesday 4:00 - 6:40
Place: 24 Hillhouse Rm 107
A detailed study of some topics in statistical decision theory, including: admissibility and minimaxity; the James-Stein estimator; Stein's unbiased estimator of risk; empirical Bayes estimators; hierarchical Bayes methods and random effects; complete class theorems; asymptotic minimaxity for nonparametric estimation; sparsity models. Prerequisite: Statistics 610.

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[ Experimental Design (STAT 613b) ]

[ Asymptotics (STAT 618a) ]

Statistical Case Studies (STAT 625a)

Instructor: John Emerson
Time: Tues, Thurs 2:30-3:45
Place: 24 Hillhouse Rm 107
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: Staff
Time: Friday 1:00-2:00
Place: 24 Hillhouse Rm 107
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. The course meets once a week all year, and students receive one half-credit each semester.

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Statistical Methods in Genetics and Bioinformatics (STAT 645b)

Instructor: Jing Zhang
Time: Tues, Thurs 10:30-11:45
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, SNP analysis, transcription regulation and sequence motif finding, and RNA/protein structure prediction.

Statistical methods will include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and some methods of classification and clustering; models will include hidden Markov models, Bayesian networks, and the coalescent. The limitations of current models, and the future opportunities for model building, will be critically addressed. Prerequisite: STAT 361, STAT 542a or b or STAT 538a. Prior knowledge of biology is not required but some interest in the subject and a willingness to carry out calculations using R will be assumed.

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[ Statistical Computing (STAT 662b) ]

[ 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: Bowers Auditorium
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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Nonparametric Statistics (STAT 680a)

Instructor: Harrison Zhou
Time: Thurs 4:00-6:00
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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Independent Study (STAT 690ab)

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

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Introduction to Research (STAT 694a)

Instructor: David Pollard
Time: Tues 4:00-6:00
Place: 24 Hillhouse Rm 107
Webpage:  http://www.stat.yale.edu/~pollard/Courses/694.fall2011/
Students will learn how to read important statistical papers and how to present seminars of professional quality.

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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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