Statistics

Faculty of the Department of Statistics

Xiao-Li Meng, Whipple V.N. Jones Professor of Statistics (Chair)
Jose Blanchet, Visiting Assistant Professor of Statistics (Columbia University)
Joseph K. Blitzstein, Assistant Professor of Statistics
Tirthankar Dasgupta, Assistant Professor of Statistics
Yingying Fan, Lecturer on Statistics
Mark E. Glickman, Visiting Associate Professor of Statistics (Boston University)
Rima Izem, Assistant Professor of Statistics (Co-Head Tutor)
S.C. Samuel Kou, John L. Loeb Associate Professor of the Natural Sciences (Director of Graduate Studies, spring term) (on leave fall term)
Yoonjung Lee, Assistant Professor of Statistics
Jun S. Liu, Professor of Statistics (on leave spring term)
Carl N. Morris, Professor of Statistics (Director of Graduate Studies)
Donald B. Rubin, John L. Loeb Professor of Statistics
Kenneth E. Stanley, Lecturer on Statistics (FAS) and Lecturer on Biostatistics (Public Health)

Other Faculty Offering Instruction in the Department of Statistics

Arthur P. Dempster, Research Professor of Theoretical Statistics
David P. Harrington, Professor of Biostatistics (Public Health) (Co-Head Tutor)
Guido W. Imbens, Professor of Economics
Xiaole Shirley Liu, Assistant Professor of Biostatistics (Public Health)
Bernard Rosner, Professor of Medicine (Medical School, Public Health)
Patrick J. Wolfe, Assistant Professor of Electrical Engineering on the Gordon McKay Endowment
Alan M. Zaslavsky, Professor of Health Care Policy (Medical School)

The Statistics Department offers four courses at the introductory level (below Statistics 110). Statistics 100 and 101 are essentially equivalent in terms of their quantitative requirements, but differ in the amount of emphasis placed on different techniques and applications. Statistics 100 emphasizes regression, including multiple regression, which is essential in economics and related fields. Statistics 101 emphasizes analysis of variance, which is widely used in experimentally-oriented subjects such as psychology and biology. Statistics 104 combines the content of Statistics 100 and 101, moving somewhat faster than these courses, and assumes a stronger quantitative orientation. Statistics 102 is comparable to Statistics 104 in its technical level, but is specifically geared toward biomedical applications and techniques.

Generally, Statistics 104 and 101 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 which courses satisfy your concentration requirements, and for guidance on selecting a course. 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
Rima Izem, David P. Harrington, 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 with Co-Head Tutors.

*Statistics 99hf. Tutorial — Senior Year
Catalog Number: 4381
Rima Izem, David P. Harrington, and members of the Department
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 with Co-Head Tutors.

For Undergraduates and Graduates

Statistics 100. Introduction to Quantitative Methods for the Social Sciences and Humanities
Catalog Number: 3808
Mark E. Glickman (Boston University) (fall term) and David P. Harrington (Public Health)
Half course (fall term; repeated spring term). Fall: Section I: M., W., F., at 10; and a section to be arranged.; Spring: Section I: M., W., F., at 11; and a section 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. When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.

Statistics 101. Introduction to Quantitative Methods for Psychology and the Behavioral Sciences.
Catalog Number: 5128
Karen Gold
Half course (spring term). M., W., F., at 3. EXAM GROUP: 8
Similar to Stat 100, but emphasizes concepts and practice of statistics used in psychology and other behavioral sciences. Topics covered: measures of central tendency and variability; development of scales used in behavioral sciences; probability; correlation and regression; estimation and hypothesis testing; analysis of variance; and chi-square tests for cross-classified data. Emphasis on translation of research questions into statistically testable hypotheses and interpretation of results in context of original research questions.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.

Statistics 102. Fundamentals of Biostatistics
Catalog Number: 0266
Bernard Rosner (Medical School, Public Health)
Half course (fall term). M., W., F., at 11, and a section to be arranged. EXAM GROUP: 4
An introduction to statistical methods used in biological and medical research. Elementary probability theory, basic concepts of statistical inference, sampling theory, regression and correlation methods, analysis of variance, and study design. Emphasis on applications to medical problems.
Note: Primarily for undergraduates with medical or biological interests. When taken for a letter grade, this course meets 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). Fall: M., W., F., at 11; Spring: M., W., F., at 11, and a section to be arranged. EXAM GROUP: 4
Similar to Stat 100, but emphasizes applications in fields including, but not limited to, economics, health sciences and policy. 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 Stat 100 and 101.
Note: Only one of the following courses may be taken for credit: Statistics 100, 101, 104. When taken for a letter grade, this course meets the Core area requirement for Quantitative Reasoning.

Statistics 105. Real-Life Statistics: Your Chance for Happiness (or Misery) - (New Course)
Catalog Number: 8782
Xiao-Li Meng
Half course (spring term). M., W., 12–1:30. EXAM GROUP: 5, 6
Discover an appreciation of statistical principles and reasoning via "Real-Life Modules" that can make you rich or poor (financial investments), loved or lonely (on-line dating), healthy or ill (clinical trials), satisfied or frustrated (chocolate/wine tasting) and more. Designed for those for whom this could be their last statistics course or those who want to be inspired to learn more from a subject that can intimately affect their chance for happiness (or misery) in life.
Prerequisite: Stat 100 or equivalent or another course in statistics with consent of the instructor.

Statistics 110. Introduction to Probability
Catalog Number: 0147
Joseph K. Blitzstein
Half course (fall term). M., W., F., at 12, and a 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, bounds, Normal, t, Binomial, Negative Binomial, Poisson, 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
Yingying Fan
Half course (spring term). M., W., F., at 12, and a section to be arranged. EXAM GROUP: 5
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: Statistics 110 and Mathematics 19a and 19b (may be taken concurrently) or equivalent.

Statistics 115. Introduction to Computational Biology and Bioinformatics
Catalog Number: 9776
Xiaole Shirley Liu (Public Health)
Half course (spring term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
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 120. Intermediate Biostatistical Methods - (New Course)
Catalog Number: 7200
Bernard Rosner (Medical School, Public Health)
Half course (spring term). M., W., F., at 11, and a section to be arranged. EXAM GROUP: 4
A survey of multivariable methods used in medical and biological research. A review of univariate inference, multiple regression, analysis of variance, nonparametric methods, logistic regression, elements of study design, survival analysis, and selected special topics in biostatistics. Emphasis on application to medical problems.
Note: Primarily for undergraduates with medical or biological interests.
Prerequisite: Either Statistics 100, 102, 104, or Statistics 110, 111.

Statistics 131. Time Series Analysis and Forecasting
Catalog Number: 8291
Yingying Fan
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
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, time domain regression approach including Box-Jenkins method and spectral analysis.
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 programming new methods.
Prerequisite: Statistics 100 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 section to be arranged.
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, including 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). Tu., Th., 1–2:30. EXAM GROUP: 15, 16
Statistical designs for estimation of treatment effects in randomized experiments. Topics include analysis of variance, randomized block and Latin square designs, balanced incomplete block designs, factorial designs, nested factorial designs, confounding in blocks, fractional replications, orthogonal arrays, response surface designs, applications in engineering, biological, and social and management sciences.
Prerequisite: Statistics 100 or equivalent and Mathematics 19a and 19b.

Statistics 149. Statistical Sleuthing through Generalized Linear Models
Catalog Number: 6617
Rima Izem
Half course (spring term). M., W., 2:30–4. EXAM GROUP: 7, 8
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
Rima Izem, 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 omitted in 2007–08. Expected to be given in 2008–09. Basic GIS skills will be covered in a short module. May not be taken for credit if Biostatistics 283 has already been taken. May not be taken concurrently with Biostatistics 283. Offered jointly with the School of Public Health as BIO 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 the instructors.

Statistics 160. Design and Analysis of Sample Surveys
Catalog Number: 2993
Alan M. Zaslavsky (Medical School)
Half course (fall term). M., W., 2:30–4. EXAM GROUP: 7, 8
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. 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
S.C. Samuel Kou
Half course (spring term). Tu., Th., 11:30–1, and a 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). Tu., Th., 1–2:30. EXAM GROUP: 15, 16
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 and Joseph K. Blitzstein
Half course (spring term). Tu., Th., 1–2:30. EXAM GROUP: 15, 16
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 - (New Course)
Catalog Number: 7864
Jose Blanchet (Columbia University)
Half course (fall term). F., 2–5. EXAM GROUP: 7, 8, 9
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, and Finance and Biology.

[Statistics 214. Causal Inference in Statistics and the Social and Biomedical Sciences]
Catalog Number: 4042
Guido W. Imbens and Donald B. Rubin
Half course (spring term). Hours to be arranged.
Approaches to causal inference. Covers randomized experiments with and without noncompliance, observational studies with and without ignorable treatment assignment, instrumental variables and sensitivity analysis. A number of applications from economics, medicine, education, etc. are discussed.
Note: Expected to be given in 2008–09.

[Statistics 215. Fundamentals of Computational Biology]
Catalog Number: 3304
Jun S. Liu
Half course (spring term). Hours to be arranged.
Developments in bioinformatics/computational biology: The basics of statistical estimation, BLAST methods and theory, cDNA sequence analysis, clustering and classification methods, data resources, hidden Markov models, Gibbs sampler, microarray analysis, gene regulatory motif discoveries, phylogenetic inference, protein structures, and comparative genomics.
Note: Expected to be given in 2008–09.

Statistics 218. Random Fields and Geometry - (New Course)
Catalog Number: 6113
Robert Adler
Half course (spring term). W., F., 1–2:30. EXAM GROUP: 6, 7
There are three parts to the course: (i) General theory of random (mainly Gaussian) processes and fields; (ii) Geometric problems generated by random fields using techniques developed over the past five years; and (iii) Applications of the new theory.

Statistics 220. Bayesian Data Analysis
Catalog Number: 6270
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 conclusions to changes in models.
Note: Emphasis throughout term on drawing inferences via computer simulation rather than mathematical analysis.
Prerequisite: Statistics 110 and 111.

Statistics 221. Applied Bayesian Statistical Computing
Catalog Number: 5959
Andrew Gelman
Half course (spring term). M., 11:30–2:30. EXAM GROUP: 4, 5
Computing methods commonly used in statistics: Generation of random numbers, Monte Carlo methods, optimization methods, numerical integration and advanced Bayesian computational tools such as the Gibbs sampler, Metropolis Hastings, method of auxiliary variables, marginal and conditional data augmentation, slice sampling, exact sampling and reversible jump MCMC.
Note: Computer programming exercises apply the methods discussed in class.
Prerequisite: Linear algebra, Statistics 111, and knowledge of a computer programming language. Statistics 220 is recommended.

Statistics 225. Spatial Statistics
Catalog Number: 6499
Rima Izem
Half course (fall term). M., W., 1–2:30. EXAM GROUP: 6, 7
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.

[Statistics 230. Multivariate Statistical Analysis]
Catalog Number: 5206
Joseph K. Blitzstein and Carl N. Morris
Half course (spring term). Hours to be arranged.
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 omitted in 2007–08. Expected to be given in 2008–09.
Prerequisite: Statistics 211 or equivalent.

Statistics 231. Time Series Analysis and Forecasting
Catalog Number: 7537
Yingying Fan
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
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. Incomplete Multivariate Data]
Catalog Number: 4196
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Half course (spring term). Hours to be arranged.
Methods for handling incomplete data sets with general patterns of missing data, emphasizing the likelihood-based and Bayesian approaches. Focus on the application and theory of iterative maximization methods, iterative simulation methods, and multiple imputation.
Note: Expected to be given in 2008–09.
Prerequisite: Comparable courses in probability (Statistics 110), theoretical statistics (Statistics 111), and knowledge of regression and linear algebra (Statistics 139).

Statistics 233. Matched Sampling - (New Course)
Catalog Number: 4036
Donald B. Rubin
Half course (fall term). Tu., Th., 11:30–1. EXAM GROUP: 13, 14
This course provides an accessible introduction to the study of matched sampling in economics, education, epidemiology, medicine, political science, psychology, sociology, statistics, or any field conducting empirical research to evaluate the causal effects of interventions.

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 245. Statistics and Litigation
Catalog Number: 3488
Daniel James Greiner (Law School)
Half course (spring term). Tu., Th., 5–6:30. EXAM GROUP: 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
Rima Izem
Half course (spring term). M., W., 2:30–4. EXAM GROUP: 7, 8
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 permission of the instructor.

[Statistics 251. Signal and Image Processing and Inference Using Wavelets]
Catalog Number: 3506
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Half course (fall term). Hours to be arranged.
Meets with Engineering Sciences 251, but with added emphasis on statistical modeling and inference. Theory of time-frequency/time-scale methods, methodologies for and motivated by statistical inference and missing-data problems, associated computational algorithms, and fundamental engineering applications.
Note: Expected to be given in 2008–09.
Prerequisite: Engineering Sciences 156 or equivalent, knowledge of probability theory and/or statistics at the level of Statistics 110/111 or above, and programming experience, 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. 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 271. Advanced Stochastic Processes]
Catalog Number: 0875
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Half course (fall term). Hours to be arranged.
Theory of regenerative processes, Markov processes in general state spaces, rates of convergence to stationarity, CLTs, coupling and exact-simulation using regeneration. Martingales, rare-event analysis via large deviations techniques, diffusion and jump-diffusion processes, functional central limit theorems, and stochastic calculus.
Note: Expected to be given in 2008–09.

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 - (New Course)
*Government 3009. Research Workshop in Applied Statistics

Graduate Courses of Reading and Research

*Statistics 301. Special Reading and Research
Catalog Number: 4474
Jose Blanchet (Columbia University) 5017, Joseph K. Blitzstein 5588, Tirthankar Dasgupta 5765, Arthur P. Dempster 2345, Yingying Fan 5805 (fall term only), Rima Izem 4944, S.C. Samuel Kou 4054 (on leave fall term), Yoonjung Lee 5300, Jun S. Liu 3760 (on leave spring term), Xiao-Li Meng 4023, Carl N. Morris 2178, Bernard Rosner (Medical School, Public Health) 4018, Donald B. Rubin 7966, Patrick J. Wolfe 5144 (fall term only), 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
Jose Blanchet 5017, Joseph K. Blitzstein 5588, Tirthankar Dasgupta 5765, Arthur P. Dempster 2345, Rima Izem 4944, S.C. Samuel Kou 4054 (on leave fall term), Yoonjung Lee 5300, Jun S. Liu 3760 (on leave spring term), Xiao-Li Meng 4023, Carl N. Morris 2178, Bernard Rosner (Medical School, Public Health) 4018, Donald B. Rubin 7966, Patrick J. Wolfe 5144 (fall term only), 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 and Joseph K. Blitzstein 5588
Half course (throughout the year). M., 10–12.
Required of all first-year doctoral students in Statistics.

*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 (on leave spring term) and Jose Blanchet (Columbia University) 5017
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
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.

[*Statistics 323. Computational and Statistical Methods in Finance]
Catalog Number: 4328
Jose Blanchet (Columbia University) 5017
Half course (spring term). Hours to be arranged.
Briefly reviews basic concepts and models in multi-period asset pricing theory. Emphasis on parameter estimation and calibration, as well as computational and statistical issues arising in pricing, hedging, credit risk, and insurance risk modeling.
Note: Expected to be given in 2008–09.
Prerequisite: Statistics 111 and 171 or equivalent (exposure to time series analysis at the level of Statistics 131 is useful, but not required).

*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 325. Functional Data Analysis
Catalog Number: 7747
Rima Izem
Half course (fall term). W., 2:30–4.
Statistical methods for exploration and analysis of Functional Data (sets of curves, images, or shapes) with applications in biology.

*Statistics 335. High-Dimensional Statistics - (New Course)
Catalog Number: 3319
Yingying Fan 5805
Half course (spring term). Hours to be arranged.

[*Statistics 340. Random Graph 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, includes fixed degree, power law, small world, and geometric random graphs.
Note: Expected to be given in 2008–09.

*Statistics 370. Topics in Empirical Finance
Catalog Number: 3593
Yoonjung Lee 5300
Half course (fall term). Hours to be arranged.
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 371. Advanced Applied Probability]
Catalog Number: 3595
Jose Blanchet (Columbia University) 5017
Half course (spring term). Hours to be arranged.
The study of limit theorems and efficient computational algorithms for the performance analysis and/or control of complex stochastic systems.
Note: Expected to be given in 2008–09.

*Statistics 399hf. Problem Solving in Statistics - (New Course)
Catalog Number: 1035
Carl N. Morris and members of the Department
Half course (throughout the year). Fall: W., 4:30–6 (bi-weekly).
Aimed principally at helping PhD students beyond their first year transition through the qualifying exams into research.