Yale University
Department of Statistics

Course List for 2001-2002

Primarily undergraduate courses

Director of Undergraduate Studies:  Professor Joseph Chang.

STAT 101a-106a, Introduction to Statistics (FALL)
Cross-listing: Statistics 501a-506a
Instructor: Mr. Joseph Chang and faculty from other departments.
Time: Tues, Thurs 1:00 pm - 2:15 pm
Place:  OML 202
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 Tuesday lecture, which introduces general concepts and methods of statistics, is attended by all students in Statistics 101-106 together. The course separates for Thursday lectures (sections), which 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. They do not count toward the natural sciences requirement. No prerequisites beyond high school algebra.

STAT 101a / E&EB 210a / MCDB 215a, Introduction to Statistics: Life Sciences.
Instructor: Mr. Joseph Chang/ Mr. Junhyong Kim.
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.

STAT 102a / EP&E 203a / PLSC 425a, Introduction to Statistics: Political Science.
Instructor: Mr. Joseph Chang/Mr. John Lapinski.
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.

STAT 103a / SOCY 119a, Introduction to Statistics: Sociology.
Instructor:  Mr. Joseph Chang/Mr. Eric Kostello.
An introduction to probability and statistics, with emphasis on applications to sociology.  Also SOCY 580a

STAT 104a / PSYC 201a, Introduction to Statistics: Psychology.
Instructor: Mr. Joseph Chang/Mr. Thomas Brown.
Statistical and probabilistic analysis of psychological problems presented with a unified foundation in basic statistical theory.  The problems are drawn from studies of sensory processing and perception, development, learning, and psychopathology.

STAT 105a / F&ES 205a, Introduction to Statistics: Environmental Sciences.
Instructor: Mr. Joseph Chang/Mr. Jonathan Reuning-Scherer.
An introduction to probability and statistics with emphasis on applications to forestry and environmental sciences.

STAT 106a, Introduction to Statistics: Data Analysis.
Instructor:  Mr. Joseph Chang/Mr. Nicolas Hengartner.
An introduction to probability and statistics with emphasis on data analysis.

STAT 230b, Introductory Data Analysis (SPRING)
Cross-listing: Statistics 530a, PLSC 530b
Instructor: Mr. John Hartigan
Time: Mon 2:30 - 3:45 (lecture), Wed 1:00 - 2:15, 2:30 - 3:45 (labs)
Place: Lecture:  OML 202/165 Prospect (Lecture), Room 100 (Stat Lab) 140 Prospect Street (labs)
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. Techniques are demonstrated on the computer. After Statistics 101-106.

STAT 241a, Probability Theory (FALL)
Cross-listing: Statistics/Mathematics 541a
Instructor: Mr. Marten Wegkamp
Time: Mon, Wed, Fri 9:30 - 10:20
Place:  WLH 116
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.

STAT 242b, Theory of Statistics (SPRING)
Cross-listing: Statistics 542b, Mathematics 242b
Instructor: Mr. Andrew Barron
Time: Mon, Wed, Fri 9:30 - 10:20
Place:  BCT 102/15 Prospect
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.

STAT 251b, Stochastic Processes (SPRING)
Cross-listing: Statistics 551b
Instructor: Mr. Dragan Radulovic
Time: Mon, Wed 1 - 2:15
Place:  BCT C031/15 Prospect
Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion and diffusions. Tecniques 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. After Statistics 241a or equivalent.

STAT 312a, Linear Models (FALL)
Cross-listing: Statistics 612a
Instructor: Mr. Dragan Radulovic
Time: Tues, Thurs 9:00-10:15
Place:  24 Hillhouse Avenue, Room 107
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. After Statistics 242b and Mathematics 222 or equivalents.

STAT 361b, Data Analysis (SPRING)
Cross-listing: Statistics 661b
Instructor: Mr. Nicolas Hengartner
Time: Mon, Wed 2:30 - 3:45
Place:  AKW 400/51 Prospect
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. Weekly sessions will be held in the Social Sciences Statistical Laboratory. After Statistics 242 and Mathematics 222b or 225a or b, or equivalents.

STAT 364b, Information Theory (SPRING)
Cross-listing: Statistics 664b
Instructor:  Mr. Andrew Barron
Time:    Tue, Thu 9:00 - 10:15
Place:  **Location Change**  24 Hillhouse, Room 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.

STAT 365b, Introduction to Function Estimation (SPRING)
Cross-listing: Statistics 665b
Instructor:  Mr. Nicolas Hengartner
Time:    Mon, Wed 11:30 - 12:45
Place:  24 Hillhouse, Room 107
A practical introduction to curve estimation techniques, such as non-linear regression, and non-parametric regression.  Splines, local smoothers and neural networks will be discussed and applied to data. Further topics include model selection, pattern recognition, inverse problems and density estimation.   SPLUS is used.

STAT 368b, Monte Carlo Methods (SPRING)
Cross-listing: Statistics 668b
Instructor:  Mr. Dragan Radulovic
Time:    Mon, Wed 9:00 - 10:15
Place:  24 Hillhouse, Room 107
Monte Carlo methods provide approximate solution to a variety of mathematical problems by performing random sampling experiments on a computer. This course will cover classical applications like integration, maximization, root finding as well as some modern developments related to statistics, including bootstrapping. The course will address both theory and application. Knowledge of at least one computer programming language will be required (CPSC 112 or equivalent). Additional prerequisites are MATH 120 and STAT 241 or equivalent.

STAT 374a, Analysis of Spatial and Time Series Data (FALL)
Cross-listing: Statistics 674a
Instructor:  Mr. John Hartigan
Time:    Tue, Thu 1:00 - 2:15
Place:  24 Hillhouse Avenue, Room 107
Study of statistical models that are useful for describing data collected over space or time.  Models include frequency domain and time domain analysis of time series; state space models and Kalman filters; point processes; Gibbs processes and random fields. After Statistics 241a, 242b or permission of instructor.

AM490b, Applied Math Senior Seminar and Project (SPRING)
Instructor: Mr. Joseph Chang
Time:  Wed 3:30 - 5:20
Place:  24 Hillhouse, Room 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.  Some meetings are devoted to talks by visiting applied mathematicians.

Primarily graduate courses

Director of Graduate Studies:  Associate Professor Nicolas Hengartner.

STAT 600b, Advanced Probability (SPRING)
Cross-listing: Statistics 330b
Instructor: Mr. Marten Wegkamp
Time:  Tues, Thurs 2:30 - 3:45
Place:  24 Hillhouse Avenue, Room 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.

STAT 603a, Stochastic Calculus (FALL)
Instructor:  Mr. David Pollard
Time: Tues, Thurs 10:30 am - 11:50 am
Place:  24 Hillhouse Avenue, Room 107
Martingales in discrete and continuous time, Brownian Motion, Sample path properties, predictable processes, stochastic integrals with respect to Brownian motion and semimartingales, stochastic differential equations. Applications mostly to counting processes and finance. Knowledge of measure-theoretic probability at the level of Statistics 600 is a prerequisite for the course, although some key concepts, such as conditioning, are reviewed. After Statistics 600.

STAT 610a, Statistical Inference (FALL)
Instructor: Mr. David Pollard
Time: Mon, Wed 1:00 pm - 2:20 pm
Place:  24 Hillhouse Avenue, Room 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.

STAT 625a, Statistical Case Studies (FALL)
Instructor: Mr. John Hartigan
Time:  Monday 1:00 pm - 3:30 pm
Place:  24 Hillhouse Avenue, Room 211
Thorough study of some large data sets on such topics as second-hand smoking, crashes in small cars, reticulate evolution, bloc voting, and Connecticut educational standards.

STAT 626b, Practical Work (SPRING)
Instructor: Mr. David Pollard
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician.

STAT 646a, Topics in the Statistical Analysis of Genomic Data (FALL)
Instructors: Mr. Joseph Chang and Mr. Junhyong Kim
Time: Mon, Wed 11:00 am - 12:15 pm
Place:  24 Hillhouse Avenue, Room 107
Several recently developed statistical methods have either already played an important role in the analysis of genomic and post-genomic data, or appear to be promising candidates to do so. We will study hidden Markov models, Bayesian networks, support vector machines and kernel methods, and perhaps other topics to be determined. For each topic, instructors will present introductory lectures on the statistical theory, models, and methods of analysis. Students will work on projects and present results, which may include computer implementations of the statistical techniques, analyses of biological sequence and gene expression data using available programs, and reports on research papers. Although no specific prerequisite courses are required, the course will make a substantial use of probability theory, statistics, introductory biology, and computation. Students without background in some of these areas may need to do additional work and should consult the instructors before enrolling.

STAT 680b, Nonparametric Statistics (SPRING)
Instructor:  Mr. Marten Wegkamp
Time:  Wed 10:30 - 11:30, Fri 10:30 - 12:30
Place:  24 Hillhouse, Room 107
We discuss recent theoretical developments in nonparametric regression, density estimation and classification. We introduce some basic empirical process theory and related tricks. Emphasis will be put on universal consistency and model selection. There is no required textbook, although I will use various sources: monographs by Devroye and Lugosi (2001), Van de Geer (2000), combined with articles, and ongoing research.  Prerequisite:  STAT 330b/600b.

STAT 699b, Research Seminar in Statistics (SPRING)
Instructor:  Mr. David Pollard
Time:  Thurs 10:30 - 12:30, Occasionally Mon 1:00 - 2:00 as scheduled (see more info. below)
Place:  24 Hillhouse, Room 107
An introduction to some current research topics, built around the weekly Departmental seminar.

STAT 700, Departmental Seminar
Time: Monday 4:15 pm - 5:30 pm
Important activity for all members of the department. 24 Hillhouse Avenue. See weekly seminar announcements.

Revision: 11 January 2002 KSY