Statistics 123a, Introduction to Statistical Methods and Probabilistic
Reasoning
Cross-listing: Statistics 523a
Instructor: Mr. A. Barron.
Basic concepts of statistical methods shown through examples of
statistical practice. Introduction to probabilistic reasoning,
hypothesis testing, regression. Some use of computers for data
analysis.
Time: Tue., Thur., 1:00-2:15
Statistics 200La and 200Lb (66200), Statistical Computing Laboratory
Instructor: Mr. D. Pollard.
This lab offers an introduction to the S-plus
statistical computing
environment, including features such as customized graphics, language
extensions, and interface with other languages.
Is a co-requisite for Statistics 230b, Statistics 312a
and Statistics 361a and is recommended for those
taking Statistics
242b.
The first five weeks of the course will present a rapid
introduction to the main features of Splus, which students from other
Statistics courses are welcome to audit.
[SYLLABUS]
Time: Fri., 2:30-5:00 at Stat Lab, 140 Prospect
Statistics 230b (66230), Introductory Data Analysis
Cross-listing: Statistics 530b, PLSC 530b
Instructor: Mr. N. Hengartner.
Survey of statistical methods: plots, transformations, regression,
analysis of variance,
clustering, principal components, contingency tables, and time series
analysis. Some sessions are
used to demonstrate techniques on the computers. Concurrent with
Statistics 200Lb; after or concurrent with
Statistics 123a or Psychology 200a or b or
equivalent.
[SYLLABUS & CLASS INFORMATION]
Time: Tue., Thu., 2:30-3:45
Statistics 241a (66241), Probability Theory
Cross-listing: Statistics/Mathematics 541a
Instructor: Mr. J. Hartigan.
A first course in probability theory: probability spaces, random
variables, expectations and probabilities, conditional probability,
independence, some discrete and continuous distributions, central
limit theorem, Markov chains, probabilistic modeling. After or
concurrent with Mathematics 120a or b or equivalents.
[SYLLABUS]
Time: Mon., Wed., Fri., 9:30-10:20
Statistics 242b (66242), Theory of Statistics
Cross-listing: Statistics 542b, Mathematics 242b
Instructor: Mr. M. Wegkamp.
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; Statistics 200Lb recommended.
Time: Mon., Wed., Fri., 9:30-10:20
Statistics 251b (66251), Stochastic Processes
Cross-listing: Statistics 551b
Instructor: Mr. N. Hengartner.
A study of random processes, including Markov chains, Markov random
fields, martingales, random walks, Brownian motion and diffusions.
Introduction to certain modern 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.
Time: Mon., Wed., 2:30-3:45
Statistics 312a (66312), Linear Models
Cross-listing: Statistics 612a
Instructor: Mr. M. Wegkamp.
The geometry of least squares; distribution theory for normal errors;
regression, analysis of variance, and designed experiments; numerical
algorithms (with particular reference to S-plus);
alternatives to least squares. Generalized linear models. After
Statistics 242b and
Mathematics 222 or equivalents.
Statistics 200Lb is a prerequisite.
[SYLLABUS]
Time: Tue., Thur., 10:30-11:20
Statistics 361a (66361), Data Analysis
Cross-listing: Statistics 661a
Instructor: Mr. J. Chang.
By analyzing data sets using the S-plus
statistical computing language, a selection of Statistical topics are
studied: linear and non-linear 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 242b or
equivalent. Statistics 200Lb is
a prerequisite.
[SYLLABUS]
Time: Mon., Wed., 2:20-3:45
Statistics 364b (66364), Introduction to Information Theory
Cross-listing: Statistics 664b
[Alternate years, next offered Spring 1998]
Statistics 600b (66600), Advanced Probability
Cross-listing: Statistics 330b
Instructor: Mr. A. Barron.
Measure theoretic probability, conditioning, laws of large numbers,
convergence in distribution, characteristic functions, central limit theorems,
martingales. Some knowledge of real analysis is assumed.
Time: Tue., Thur., 2:30-3:45
Statistics 606b (66606), Markov Chains
Instructor: Mr. J. Chang
Random walks, mixing times, stopping rules, threshold phenomena.
Harris chains and general state spaces. Renewal theory. Applications
to simulation and optimization. Other topics as time permits:
diffusions, potential theory, large deviations, hidden Markov models.
Prerequiste:
Statistics 600b or consent of instructor.
Time: Times to be arranged at organizational meeting
Statistics 610a (66610), Statistical Inference
Instructor: Mr. J. Chang
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.
Time: Tue., Thur., 10:30-11:45
Statistics 618b (66618), Asymptotic Theory
Instructor: Mr. D. Pollard and Mr. M. Wegkamp.
A careful introduction to asymptotic methods in mathematical statistics.
Topics include: consistency and asymptotic distributions, contiguity,
efficiency,
likelihood ratio theory, and (if time permits) Le Cam's theory for
convergence of experiments.
After Statistics 600b and Statistics 610b
Time: Times to be arranged at organizational meeting
Statistics 625a (66625), Statistical Case Studies
Instructor: Mr. D. Pollard.
Thorough analysis of complex data sets using S-plus, with emphasis on
the balance between graphical techniques and formal inferential
procedures. Interpretations and use of Census data and mapping software.
The course will
focus on the analysis of a huge data set related to a consulting problem on
jury selection.
[SYLLABUS]
Time: Tuesday 3:00-5:00
Statistics 626b (66626), Practical Work
Instructor: Staff.
Individual one-semester projects, with students working on studies
outside the Department, under the guidance of a statistician.
Time: Times to be arranged at organizational meeting.
Statistics 685a (66685), Classification
Instructor: Mr. J. Hartigan.
Statistical methods of identifying classes, types and clusters, uses of
classification in prediction and inference. Recognition, k-means, minimum
spanning trees, hierarchical clustering algorithms, density estimation;
model estimation. Mixture models, product partition models, excess mass
models change point models, block clustering models, percolation.
Applications to reticulate evolution, mammalian teeth, parliamentary
voting, subtypes of schizophrenia, and foundations of probability.
Time: Times to be arranged at organizational meeting
Statistics 690a (66690), Introduction to Research
Instructor: Mr. A. Barron
Formation and development of research topics. Students will read and
review literature and give oral presentations. Discussion of methods to
address open problems in statistics.
Time: Times to be arranged at organizational meeting.
Statistics 700, Departmental Seminar
Important activity for all members of the department. Either at
24 Hillhouse Avenue or at EPH. See
weekly seminar announcements.
Time: Monday 4:15-
Revision: 3 Jan 97, sjm