Statistics

Faculty of the Department of Statistics

Xiao-Li Meng, Whipple V.N. Jones Professor of Statistics (Chair)
Alan Agresti, Visiting Professor of Statistics (University of Florida) (fall term only)
Edoardo Maria Airoldi, Assistant Professor of Statistics
Joseph K. Blitzstein, Assistant Professor of Statistics (Co-Director of Undergraduate Studies)
Stephen James Blyth, Lecturer on Statistics (spring term only)
Richard J. Cleary, Visiting Professor of Statistics (fall term only)
Tirthankar Dasgupta, Assistant Professor of Statistics (Co-Director of Graduate Studies)
Paul Edlefsen, Lecturer on Statistics
S.C. Samuel Kou, Professor of Statistics (on leave 2009-10)
Yoonjung Lee, Assistant Professor of Statistics
Julia Yi-Hsin Lin, Lecturer on Statistics (Medical School)
Jun S. Liu, Professor of Statistics (FAS), Professor in the Department of Biostatistics (Public Health)
Carl N. Morris, Professor of Statistics (Co-Director of Graduate Studies)
Kevin Andrew Rader, Preceptor in Statistics
Donald B. Rubin, John L. Loeb Professor of Statistics
Kenneth E. Stanley, Lecturer on Statistics

Other Faculty Offering Instruction in the Department of Statistics

Arthur P. Dempster, Professor of Theoretical Statistics, Emeritus
Mark E. Glickman, Visiting Associate Professor of Statistics (Boston University) (spring term only)
Daniel James Greiner, Assistant Professor of Law (Law School)
David P. Harrington, Professor of Biostatistics (Public Health) (Co-Director of Undergraduate Studies)
Xiaole Shirley Liu, Associate Professor of Biostatistics (Public Health)
Bernard Rosner, Professor of Medicine (Biostatistics)
Patrick J. Wolfe, Associate Professor of Electrical Engineering on the Gordon McKay Endowment
Alan M. Zaslavsky, Professor of Health Care Policy (Medical School)

Statistics is a relatively young discipline organized around the rapidly growing body of knowledge about quantitative methods for the analysis of data, the making of rational decisions under uncertainty, the design of experiments, and the modeling of randomness and variability in the social and natural sciences.

A basic introduction to the field is provided by any of Statistics 100 through 104, which introduce statistical principles (without any mathematical or statistical prerequisite), with different areas of application emphasized as indicated in the descriptions. Statistics 100, 101, and 104 are Gen Ed-eligible, within the Empirical and Mathematical Reasoning category.

Empirical and Mathematical Reasoning 16: Real Life Statistics: Your Chance at Happiness (or Misery) is a newly designed Gen Ed course introducing statistical principles and reasoning as they arise in everyday life, organized through modules on various areas of application such as health, wine-tasting, and finance.

An introduction to probability and statistics at a higher mathematical and theoretical level is provided by Statistics 110 together with Statistics 111. These courses provide a foundation for understanding random variables, statistical models, and statistical inference, and are prerequisites for most of the department’s more advanced courses.

Statistics 101 and 104 will be accepted as fulfilling any requirement or prerequisite that is fulfilled by Statistics 100. Consult the Statistics Department or your tutorial office for more information about courses that satisfy your concentration requirements or for guidance on selecting courses. More detailed information can be accessed at the Statistics Department website: www.stat.harvard.edu.

Primarily for Undergraduates

*Statistics 91r. Supervised Reading and Research
Catalog Number: 6641
Joseph K. Blitzstein, David P. Harrington (Public Health), and members of the Department
Half course (fall term; repeated spring term). Hours to be arranged.
Note: Normally may not be taken more than twice; may be counted for concentration in Statistics, if taken for graded credit; may be taken in either term; for further information consult Co-Directors of Undergraduate Studies.

*Statistics 99hf. Tutorial — Senior Year
Catalog Number: 4381
Joseph K. Blitzstein and David P. Harrington (Public Health)
Half course (throughout the year). Hours to be arranged.
The systematic application of statistical ideas to a problem area.
Note: In exceptional circumstances, may be taken as a half course in the spring term only; for further information consult Co-Directors of Undergraduate Studies.

For Undergraduates and Graduates

Statistics 100. Introduction to Quantitative Methods for the Social Sciences and Humanities
Catalog Number: 3808
Paul Edlefsen (fall term) and David P. Harrington (Public Health) (spring term)
Half course (fall term; repeated spring term). Fall: M., W., F., at 10, and weekly sections to be arranged; Spring: M., W., F., at 11, and weekly sections to be arranged. EXAM GROUP: Fall: 3; Spring: 4
Introduction to key ideas underlying statistical and quantitative reasoning. Topics covered: methods for organizing, summarizing and displaying data; elements of sample surveys, experimental design and observational studies; methods of parameter estimation and hypothesis testing in one- and two-sample problems; regression with one or more predictors; correlation; and analysis of variance. Explores applications in a wide range of fields, including the social and political sciences, medical research, and business and economics.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. This course, when taken for a letter grade, meets the General Education requirement for Empirical and Mathematical Reasoning or the Core area requirement for Quantitative Reasoning.

Statistics 101. Introduction to Quantitative Methods for Psychology and the Behavioral Sciences
Catalog Number: 5128
Alan Agresti (University of Florida) (fall term) and Julia Yi-Hsin Lin (Medical School) (spring term)
Half course (fall term; repeated spring term). Fall: Tu., Th., 10–11:30; Spring: Tu., Th., 11:30–1. EXAM GROUP: Fall: 12, 13; Spring: 13, 14
Similar to Statistics 100, but emphasizes concepts and practice of statistics used in psychology and other social and behavioral sciences. Topics covered: describing center and variability; probability and sampling distributions; estimation and hypothesis testing for comparing means and comparing proportions; contingency tables; correlation and regression; multiple regression; analysis of variance. Emphasis on translation of research questions into statistically testable hypotheses and models, and interpretation of results in context.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. This course, when taken for a letter grade, meets the General Education requirement for Empirical and Mathematical Reasoning or the Core area requirement for Quantitative Reasoning.

Statistics 104. Introduction to Quantitative Methods for Economics
Catalog Number: 4582
Kenneth E. Stanley (FAS, Public Health)
Half course (fall term; repeated spring term). Lecture 1: M., W., F., at 11; Lecture 2: M., W., F., at 1, and weekly sections to be arranged. EXAM GROUP: 4
Similar to Statistics 100, but emphasizes applications in fields including, but not limited to, economics, health sciences and policy analysis. Topics covered: descriptive and summary statistics for both measured and counted variables; elements of experimental and survey design; probability; and statistical inference including estimation and tests of hypotheses as applied to one- and two-sample problems, multiple regression, correlation, and analysis of variance. Taught at a slightly higher level than Statistics 100 and 101.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. This course, when taken for a letter grade, meets the General Education requirement for Empirical and Mathematical Reasoning or the Core area requirement for Quantitative Reasoning.

Statistics 110. Introduction to Probability
Catalog Number: 0147
Joseph K. Blitzstein
Half course (fall term). M., W., F., at 12, and a weekly section to be arranged. EXAM GROUP: 5
A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes’ Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.
Note: When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.
Prerequisite: Mathematics 19a or equivalent or above required (may be taken concurrently), Mathematics 19b or equivalent or above recommended.

Statistics 111. Introduction to Theoretical Statistics
Catalog Number: 1836
Edoardo Maria Airoldi
Half course (spring term). Tu., Th., 1-2:30, and a weekly section to be arranged. EXAM GROUP: 15, 16
Basic concepts of statistical inference from frequentist and Bayesian perspectives. Topics include maximum likelihood methods, confidence and Bayesian interval estimation, hypothesis testing, least squares methods and categorical data analysis.
Prerequisite: Mathematics 19a and 19b or equivalent and Statistics 110.

Statistics 115. Introduction to Computational Biology and Bioinformatics
Catalog Number: 9776
Xiaole Shirley Liu (Public Health) and Jun S. Liu
Half course (spring term). Tu., Th., 10–11:30. EXAM GROUP: 12, 13
Basic problems, algorithms and data analysis approaches in computational biology. Topics include sequence alignment, genome sequencing and gene finding, gene expression microarray analysis, transcription regulation and sequence motif finding, comparative genomics, RNA/protein structure prediction, proteomics and SNP analysis. Computational algorithms covered include hidden Markov model, Gibbs sampler, clustering and classification methods.
Prerequisite: Good quantitative skills, strong interest in biology, willingness and diligence to learn programming.

Statistics 123. Applied Quantitative Finance on Wall Street - (New Course)
Catalog Number: 71785
Stephen James Blyth
Half course (spring term). Tu., Th., 2:30–4. EXAM GROUP: 16, 17
An introduction to modern financial markets, and the probabilistic and statistical techniques used to navigate them. Methodology will be motivated wherever possible by real problems from the financial industry. Topics include: interest-rates, swap markets and fixed income securities; structured note construction and valuation; options markets and probabilistic approaches to valuation; electronic trading and performance evaluation. Designed for those seeking a basic understanding of the evolution of quantitative challenges on Wall Street, and the tool-kit developed to address them.
Prerequisite: Statistics 110 or equivalent.

Statistics 131. Time Series Analysis and Forecasting
Catalog Number: 8291
Tirthankar Dasgupta
Half course (fall term). M., W., 1–2:30. EXAM GROUP: 6, 7
An introduction to time series models and associated methods of data analysis and inference. Auto regressive (AR), moving average (MA), ARMA, and ARIMA processes, stationary and non-stationary processes, seasonal processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, spectral analysis, and transfer function models.
Prerequisite: Statistics 111 and 139 or equivalent.

Statistics 135. Statistical Computing Software
Catalog Number: 3451
Steven Richard Finch
Half course (fall term). M., W., F., at 10. EXAM GROUP: 3
An introduction to major statistics packages used in academics and industry (SAS and R). Will discuss data entry and manipulation, implementing standard analyses and graphics, exploratory data analysis, simulation-based methods, and new programming methods.
Note: Primarily for Statistics AM students.
Prerequisite: Statistics 110 or 139 (may be taken concurrently) or with permission of instructor.

Statistics 139. Statistical Sleuthing Through Linear Models
Catalog Number: 1450
Yoonjung Lee
Half course (fall term). Tu., Th., 10-11:30, and a weekly section to be arranged. EXAM GROUP: 12, 13
A serious introduction to statistical inference where linear models and related methods are used. Topics include the pros and cons of t-tools and their alternatives, multiple-group comparisons, linear regressions, model checking and refinement. Emphasis on statistical thinking and tools for real-life problems, application to current events whenever relevant.
Prerequisite: Statistics 100 or equivalent and Mathematics 19a and 19b or equivalent.

Statistics 140. Design of Experiments
Catalog Number: 7112
Tirthankar Dasgupta
Half course (spring term). M., W., 2:30–4. EXAM GROUP: 7, 8
Statistical designs for efficient experimentation in physical, chemical, biological, social and management sciences and in engineering. A systematic approach to explore input-output relationships by deliberately manipulating input variables. Topics include analysis of variance, completely randomized and randomized block designs, Latin square designs, balanced incomplete block designs, factorial designs, confounding in blocks, fractional replications, orthogonal arrays, and response surface designs. Each topic is motivated by a real-life example.
Prerequisite: Statistics 100 or equivalent and Mathematics 19a and 19b.

Statistics 149. Statistical Sleuthing through Generalized Linear Models
Catalog Number: 6617
Mark E. Glickman (Boston University)
Half course (spring term). M., W., 1–2:30. EXAM GROUP: 6, 7
A sequel to Statistics 139, emphasizing common methods for analyzing categorical data. Topics include mixed effects model, contingency tables, log-linear models, logistic, Probit and Poisson regression, model selection, and model checking. Examples will be drawn from several fields, particularly from biology and social sciences.
Prerequisite: Statistics 139 or permission of instructor.

[Statistics 155. Spatial Statistics for Social Inquiry and Health Research]
Catalog Number: 1993
Christopher J. Paciorek (Public Health) and Louise M. Ryan (Public Health)
Half course (spring term). Hours to be arranged.
Introduction to spatial statistics as applied to social science and public health. Emphasizes analysis and visualization methods for areal data, geostatistical data, and point processes. Practical focus on case studies, guest lectures, and student projects.
Note: Expected to be given in 2010–11. Basic GIS skills will be covered in a short module. Offered jointly with the School of Public Health as BIO 283. May not be taken for credit if Biostatistics 283 has already been taken. May not be taken concurrently with Biostatistics 283.
Prerequisite: Coursework or equivalent experience in regression at the level of Statistics 139 or 149, Economics 1123, Psychology 1951, Biostatistics 210, 211, or 213, and coursework or equivalent experience in statistical programming such as Statistics 135 or Biostatistics 503 or permission of instructors. Prerequisites are guidelines and students are encouraged to consult instructors.

Statistics 160. Design and Analysis of Sample Surveys
Catalog Number: 2993
Alan M. Zaslavsky (Medical School)
Half course (fall term). M., W., 2:30–5. EXAM GROUP: 7, 8, 9
Methods for design and analysis of sample surveys. The toolkit of sample design features and their use in optimal design strategies. Sampling weights and variance estimation methods, including resampling methods. Brief overview of nonstatistical aspects of survey methodology such as survey administration and questionnaire design and validation (quantitative and qualitative). Additional topics: calibration estimators, variance estimation for complex surveys and estimators, nonresponse, missing data, hierarchical models, and small-area estimation.
Prerequisite: Statistics 111 or 139 or permission of instructor.

Statistics 170. Introduction to Quantitative Methods in Finance
Catalog Number: 1202
Yoonjung Lee
Half course (spring term). Tu., Th., 10-11:30, and a weekly section to be arranged. EXAM GROUP: 12, 13
Introduces stochastic analysis tools to be used as a basis for developing continuous-time asset pricing theory. Various quantitative methods widely used in the financial industry for valuing derivative products will be presented: binomial-tree valuation methods, extensions of the Black-Scholes option pricing formula, numerical techniques for solving partial differential equations, and Monte Carlo simulations.
Prerequisite: Statistics 110 and 111 or equivalent.

Statistics 171. Introduction to Stochastic Processes
Catalog Number: 4180
Jun S. Liu
Half course (spring term). Tu., Th., 11:30-1, and a weekly section to be arranged. EXAM GROUP: 13, 14
An introductory course in stochastic processes. Topics include Markov chains, branching processes, Poisson processes, birth and death processes, Brownian motion, martingales, introduction to stochastic integrals, and their applications.
Prerequisite: Statistics 110 or equivalent.

Primarily for Graduates

Statistics 210. Probability Theory
Catalog Number: 2487
Carl N. Morris and Joseph K. Blitzstein
Half course (fall term). M., W., 2:30–4; Th., 4–6. EXAM GROUP: 7, 8
Random variables, measure, representations. Families of distributions: Multivariate Normal, conjugate, marginals, mixtures. Conditional distributions and expectation. Convergence, laws of large numbers, and central limit theorems. Markov chains and martingales.
Prerequisite: Statistics 110 or equivalent required; Statistics 111 or equivalent recommended.

Statistics 211. Statistical Inference
Catalog Number: 1946
Carl N. Morris
Half course (spring term). M., W., 2:30–4. EXAM GROUP: 7, 8
Inference: frequency, Bayes, decision analysis, foundations. Likelihood, sufficiency, and information measures. Models: Normal, exponential families, multilevel, and non-parametric. Point, interval and set estimation; hypothesis tests. Computational strategies, large and moderate sample approximations.
Prerequisite: Statistics 111 and 210 or equivalent.

[Statistics 212. Probability and Mathematical Statistics III: Special Topics]
Catalog Number: 7864
Instructor to be determined
Half course (fall term). F., 2–5.
Contemporary probabilistic techniques for analysis of stochastic processes commonly used in applied probability. Studies functional weak convergence analysis and large deviations results (both for light- and heavy-tailed systems). Applications: Queueing, Risk Theory, Finance, and Biology.
Note: Expected to be given in 2010–11.

Statistics 215. Introduction to Computational Biology and Bioinformatics - (New Course)
Catalog Number: 29169
Xiaole Shirley Liu (Public Health) and Jun S. Liu
Half course (spring term). Tu., Th., 10–11:30. EXAM GROUP: 12, 13
Meets with Statistics 115, but graduate students are required to complete a research project and make a final presentation during reading period in addition to completing all work assigned for Statistics 115.
Prerequisite: Good quantitative skills, strong interest in biology, good programming skills in C/C++, Java, Perl or Python.

Statistics 220. Bayesian Data Analysis
Catalog Number: 6270
Donald B. Rubin and Jun S. Liu
Half course (fall term). Tu., Th., 2:30-4. EXAM GROUP: 16, 17
Basic Bayesian models, followed by more complicated hierarchical and mixture models with nonstandard solutions. Includes methods for monitoring adequacy of models and examining sensitivity of models.
Note: Emphasis throughout term on drawing inferences via computer simulation rather than mathematical analysis.
Prerequisite: Statistics 110 and 111.

Statistics 221. Statistical Computing and Learning
Catalog Number: 5959
Edoardo Maria Airoldi
Half course (fall term). Tu., Th., 1–2:30. EXAM GROUP: 15, 16
Computational methods commonly used in statistics: random number generation, optimization methods, numerical integration, Monte Carlo methods including Metropolis-Hastings and Gibbs samplers, approximate inference techniques including Expectation-Maximization algorithms, Laplace approximation and variational methods, data augmentation strategies.
Note: Computer programming exercises will apply the methods discussed in class.
Prerequisite: Linear algebra, Statistics 111, and knowledge of a computer programming language (R or Matlab) required; Statistics 220 recommended.

[Statistics 225. Spatial Statistics]
Catalog Number: 6499
Instructor to be determined
Half course (fall term). Hours to be arranged.
Introduction to three types of spatial data: point pattern, geospatial, and lattice. For each type of data, presentation and application of statistical and computational methods for description, modeling, and analysis.
Note: Expected to be given in 2010–11.

[Statistics 230. Multivariate Statistical Analysis]
Catalog Number: 5206
Edoardo Maria Airoldi
Half course (spring term). M., W., 1–2:30.
Multivariate inference and data analysis. Advanced matrix theory and distributions, including Multivariate Normal, Wishart, and multilevel models. Supervised learning: multivariate regression, classification, and discriminant analysis. Unsupervised learning: dimension reduction, principal components, clustering, and factor analysis.
Note: Expected to be given in 2010–11.
Prerequisite: Statistics 211 or equivalent.

Statistics 231. Time Series Analysis and Forecasting
Catalog Number: 7537
Tirthankar Dasgupta
Half course (fall term). M., W., 1–2:30. EXAM GROUP: 6, 7
Meets with Statistics 131, but graduate students will be exposed to a more rigorous treatment of time series analysis.
Prerequisite: Statistics 111 and 139 or equivalent.

[Statistics 232 (formerly *Statistics 332). Topics in Missing Data]
Catalog Number: 9483
Donald B. Rubin
Half course (spring term). W., 10–12:30.
Note: Expected to be given in 2010–11.

Statistics 239. Statistical Sleuthing Through Linear Models
Catalog Number: 8433
Yoonjung Lee
Half course (fall term). Tu., Th., 10-11:30 and a weekly section to be arranged. EXAM GROUP: 12, 13
Meets with Statistics 139, but graduate students will be required to complete additional assignments designed to cover theoretical aspects of regression analysis.

Statistics 240 (formerly Statistics 233). Matched Sampling and Study Design
Catalog Number: 4036
Donald B. Rubin and Tirthankar Dasgupta
Half course (fall term). W., 5–7 p.m. EXAM GROUP: 9
This course provides an accessible introduction to the study of matched sampling and other design techniques in any field (e.g., economics, education, epidemiology, medicine, political science, etc.) conducting empirical research to evaluate the causal effects of interventions.
Prerequisite: Statistics 110, Statistics 111 and Statistics 139.

Statistics 245. Statistics and Litigation
Catalog Number: 3488
Daniel James Greiner (Law School)
Half course (spring term). Tu., W., 5–7 p.m. EXAM GROUP: 9, 18
Students work in teams with law students to analyze data, prepare expert reports, and give testimony. Course teaches how to analyze data, present results to untrained but intelligent users, and defend conclusions.
Prerequisite: A graduate course in data analysis, such as Statistics 220, Government 2001, or Economics 2120

Statistics 249. Statistical Sleuthing Through Generalized Linear Models
Catalog Number: 3987
Mark E. Glickman (Boston University)
Half course (spring term). M., W., 1–2:30. EXAM GROUP: 6, 7
Meets with Statistics 149, but graduate-level covers supplementary topics such as Bayesian analysis for generalized linear models and generalized mixed effect models. Requires extra homework and examination problems in addition to those for Statistics 149.
Prerequisite: Statistics 139, Statistics 220 or Statistics 221, or by permission of instructor.

Statistics 260. Design and Analysis of Sample Surveys - (New Course)
Catalog Number: 59588
Alan M. Zaslavsky (Medical School)
Half course (fall term). M., W., 2:30–5.
Meets with Stat 160, but graduate students will have an extended class period and complete additional assignments for a more theoretical, in-depth treatment of topics.
Prerequisite: Statistics 110, 111, and 139 or permission of instructor.

Statistics 270. Introduction to Quantitative Methods in Finance
Catalog Number: 3518
Yoonjung Lee
Half course (spring term). Tu., Th., 10-11:30, and a weekly section to be arranged. EXAM GROUP: 12, 13
Meets with Statistics 170, but graduate students will be exposed to a more rigorous treatment of stochastic calculus.
Prerequisite: Statistics 110 and 171 or equivalent.

Statistics 285r (formerly Statistics 385). Statistical Machine Learning
Catalog Number: 0512
Edoardo Maria Airoldi
Half course (spring term). F., 2–4:30. EXAM GROUP: 7, 8, 9
Hands-on introduction to concepts and computations central to cutting-edge, including sparse learning in high dimensions, semi-supervised learning strategies, structured predictions, approximate inference strategies, analysis of complex graphs. The material is delivered via extensive case studies.
Note: Computer programming exercises will apply the methods discussed in class.
Prerequisite: Familiarity with estimation and inference techniques, and knowledge of a computer programming language (R or Matlab) required; Statistics 220 or 221 recommended.

Cross-listed Courses

Biostatistics 244. Analysis of Failure Time Data
*Biostatistics 250. Probability Theory and Applications II
Economics 1127. Statistical Methods for Evaluating Causal Effects
*Government 3009. Research Workshop in Applied Statistics

Graduate Courses of Reading and Research

*Statistics 301. Special Reading and Research
Catalog Number: 4474
Edoardo Maria Airoldi 6132, Joseph K. Blitzstein 5588, Tirthankar Dasgupta 5765, Arthur P. Dempster 2345, S.C. Samuel Kou 4054, Yoonjung Lee 5300, Jun S. Liu 3760, Xiao-Li Meng 4023, Carl N. Morris 2178, Bernard Rosner (Medical School, Public Health) 4018, Donald B. Rubin 7966, Patrick J. Wolfe 5144, and Alan M. Zaslavsky (Medical School) 1927
Half course (fall term; repeated spring term). Hours to be arranged.

*Statistics 302. Direction of Doctoral Dissertations
Catalog Number: 3382
Edoardo Maria Airoldi 6132, Joseph K. Blitzstein 5588, Tirthankar Dasgupta 5765, Arthur P. Dempster 2345, S.C. Samuel Kou 4054, Yoonjung Lee 5300, Jun S. Liu 3760, Xiao-Li Meng 4023, Carl N. Morris 2178, Bernard Rosner (Medical School, Public Health) 4018, Donald B. Rubin 7966, Patrick J. Wolfe 5144, and Alan M. Zaslavsky (Medical School) 1927
Half course (fall term; repeated spring term). Hours to be arranged.

*Statistics 303hf. The Art and Practice of Teaching Statistics
Catalog Number: 3545
Xiao-Li Meng 4023, Joseph K. Blitzstein 5588 (spring term only), and Tirthankar Dasgupta 5765
Half course (throughout the year). M., 10–12.
Required of all first-year doctoral students in Statistics.

*Statistics 305. Statistical Fallacies and Paradoxes: A Cartoon Guide (Graduate Seminar in General Education) - (New Course)
Catalog Number: 56883
Xiao-Li Meng 4023
Half course (fall term). Tu., 6–8:30 p.m.
Explores the principles behind paradoxes and the fundamentals behind fallacies through research, discussion, and the development of pedagogical resources for a General Education class. With a focus on communicating statistical thinking clearly and creatively, this course investigates common points of conceptual confusion and historical connections to misinformation in science, social policy, and the media. Students will design a "Cartoon Guide" of General Education materials to deepen their own understanding and ability to educate.

*Statistics 310hfr. Topics in Astrostatistics
Catalog Number: 2105
Xiao-Li Meng 4023
Half course (throughout the year). Tu., 11:30–1.

*Statistics 311. Monte Carlo Methods in Scientific Computing
Catalog Number: 0826
Jun S. Liu 3760
Half course (fall term). Hours to be arranged.
Prerequisite: Statistics 220 or equivalent.

[*Statistics 321. Stochastic Modeling and Bayesian Inference]
Catalog Number: 4060
S.C. Samuel Kou 4054 (on leave 2009-10)
Half course (spring term). Hours to be arranged.
Stochastic processes and their applications in biological, chemical and financial modeling. Bayesian inference about stochastic models based on the Monte Carlo sampling approach.
Note: Expected to be given in 2010–11.

*Statistics 324r. Parametric Statistical Inference and Modeling
Catalog Number: 3366
Carl N. Morris 2178
Half course (spring term). Th., 3–5.
Theory of multi-level parametric models, including hidden Markov models, and applications likely to include biostatistics, health services, education, and sports.

*Statistics 340. Random Network Models
Catalog Number: 1650
Joseph K. Blitzstein 5588
Half course (spring term). Hours to be arranged.
Random graph models for biological, social, and information networks, including fixed degree, exponential, power law, small world, and geometric random graphs. Estimation and sampling methods for network data.

[*Statistics 341. Advanced Topics in Experimental Design]
Catalog Number: 9827
Tirthankar Dasgupta 5765
Half course (fall term). W., 2–4.
Note: Expected to be given in 2010–11.

*Statistics 370. Topics in Empirical Finance
Catalog Number: 3593
Yoonjung Lee 5300
Half course (spring term). M., 2:30–4.
Exposes students to a variety of topics in Empirical Finance, including high frequency data analysis, high-dimensional volatility estimation, continuous-time stochastic modeling, and non-linear filtering.

*Statistics 399hf. Problem Solving in Statistics
Catalog Number: 1035
Carl N. Morris 2178
Half course (throughout the year). W., 4:30–6.
Aimed principally at helping Statistics PhD students beyond their first year transition through the qualifying exams into research.