Statistics Department
Courselist for Fall 2008/Spring 2009

• Organizational Meeting for all term courses whose times are not listed below: .
• 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   [this year]    2009-10   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. 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: Andrew Barron
• Typical 1st-year courses 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: TBA
Webpage: 
Every day we are inundated with data. How do we recognize dishonest or even unintentionally distorted representations of quantitative information? How can we reconcile two medical studies with seemingly contradictory conclusions? How many observations do we need in order to make a sound decision? This course introduces statistical reasoning, emphasizing how Statistics can help us understand the world. 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 Excel and reach conclusions about 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
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 Mokshay Madiman
Time: Tues, Thurs 1:00 -2:15
Place: TBA
Webpage: 

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Real-World Statistics(STAT 128b)

Instructor: Jay Emerson
Time: Tues, Thurs 9:00-10:15
Place: 
Webpage: 
Quantitative exploration of real-world problems through analysis of data. Topics include nationalistic biases in Olympic judging of diving and gymnastics; property tax assessments in New Haven, CT; the role of the random selection of judges in international figure skating competitions; the 2006 stock option back dating scandal; and hte study of bias in the jury selection process of Connecticut's Federal Court.

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Statistics as a Way of Knowing(STAT 129a/PSYC 129a)

Instructor: Nelson Donegan
Time: Tues, Thurs 11:35 - 12:50
Place: WALL 81
Webpage: 
An introduction to basic concepts of statistics and probability that allow us to describe, evaluate, and understand aspects of the world and make informed choices. Exploration of relationships among statistical reasoning, cognitive psychology, and philosophical theories of knowledge.

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

Instructor: Staff
Time: Mon, Wed 2:30 - 3:45
Place: PR 140 STATLAB
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 104 (tentative)
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: Hannes Leeb
Time: Mon, Wed, Fri 9:25 - 10:15
Place: WLH 208 (tentative)
Webpage:  http://www.stat.yale.edu/~hz68/241/
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 242a/542a)

Instructor: Andrew Barron
Time: TBA
Place: TBA
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: Harrison Zhou
Time: Mon, Wed, Fri 9:25 - 10:15
Place: TBA
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: David Pollard
Time:  Mon, Wed 1:00 - 2:15
Place: TBA
Webpage:  http://www.stat.yale.edu/~pollard/Courses/251.spring09
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: Hannes Leeb
Time: Tues, Thurs 9:00-10:15
Place: TBA
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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Data analysis(STAT 361a/STAT 661a)

Instructor: Lisha Chen
Time: Mon, Wed 2:30 - 3:45
Place: TBA
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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Information theory(STAT 364b/STAT 664b)

Instructor: Hannes Leeb
Time: Tues, Thurs 9:00 - 10:15
Place: 24 Hillhouse
Webpage:  http://www.stat.yale.edu/~arb4/stat364/2008coursehomepage.txt
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
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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Applied Math Senior Seminar and Project (AM490b)

Instructor: Andrew Barron
Time: Wed 3:30-5:20
Place: 24 Hillhouse
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 Probability(STAT 600b/STAT 330b)

Instructor: David Pollard
Time: Tues, Thurs 2:30 - 3:45
Place: TBA
Webpage:  http://www.stat.yale.edu/~pollard/Courses/600.spring09
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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Stochastic Calculus(STAT 603a)

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

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Statistical Inference(STAT 610a)

Instructor: Mokshay Madiman
Time: Tues, Thurs 10:30-11:45
Place: 24 Hillhouse
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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Experimental Design(STAT 613b)

Instructor: Timothy Gregoire and Jonathan Reuning-Scherer
Time: TBA
Place: TBA
Webpage: 
Principles of design for planned experiements, coupled with methods of analysis of experimental data. Strengths and weakness of block, split-plot, and completely randomized designs; extensive analysis of data that designs produce. Questions of sample size estimation. Prerequisite: an introductory couse in statistics.

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Random Matrices in Statistics(STAT 617b)

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

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Asymptotics(STAT 618a)

Instructor: David Pollard
Time: TBA
Place: TBA
Webpage:  http://www.stat.yale.edu/~pollard/Courses/618.fall08
A careful study of some standard asymptotic techniques in statistics and econometrics, and their modern refinements. Topics selected from classical likelihood theory and M-estimation; empirical process methods; concentration inequalities; semiparametric models; local asymptotic normality; concepts of efficiency. Prerequisites: knowledge of probability at the level of STAT 600b.

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

Instructor: Harrison Zhou
Time: TBA
Place: TBA
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, Fri 10:30 - 11:45
Place: 24 Hillhouse
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: Jay Emerson
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, Lisha Chen
Time: Friday 2:00 - 4:00
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: Joseph Chang
Time: Tues, Thurs 10:30-11:45
Place: 24 Hillhouse
Webpage: 
Stochastic modeling and statistical methods applied to problems such as mapping quantitative trait loci, analyzing gene expression data, sequence alignment, and reconstructing evolutionary trees. Statistical methods include maximu likelihood, Bayesian inference, Monte Carlo Markov chains, and some methods of classification and clustering. Models introduced include variance components, hidden Markov models, Bayesian networks, and coalescent. Recommended background: Stat 541, Stat 542. Prior knowledge of biology is not required.

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Multivariate Statistics for Social Sciences(STAT 660b)

Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00 - 2:15
Place: TBA
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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Statistical Computing(STAT 662a)

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

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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 675a)

Instructor: Lisha Chen
Time: TBA
Place: TBA
Webpage: 
Unsupervised learning, distinguished from supervised learning, is concerned with exploring data structure and extracting meaningful information from data without the guidance of a particular variable of interest. This course will be focused on two subfields of unsupervised learning, dimension reduction and clustering analysis. We cover both classical and recently developed methods concerning these areas. Applications arising from image processing, text mining and bioinformatics will be discussed. This graduate level course can be taken by qualified undergraduates with permission. After STAT 542a or b or STAT 538a. Students in all fields are welcome.

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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: Jay Emerson
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: TBA
Place: 24 Hillhouse basement
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.html
Important activity for all members of the department. See webpage for weekly seminar announcements.

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