Nicolas Hengartner (Acting Director/Fall)
Statistics 101-103, (66101,66103), Introduction to Statistics (FALL)
Cross-listing: Statistics 501a-503a
Instructor: Mr. A. Barron. TTh 1:00 pm - 2:15 pm
Each of these courses gives a basic introduction to statistics,
requiring no mathematics beyond high school algebra. Topics include
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 101-103 together. The course separates for
Thursday lectures, 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.
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Statistics 102aG - EP & E 203a - Political Science 425a (66102-02) Introduction to Statistics: Social Sciences.
Instructor: Mr. Andrew Barron, Mr. Donald Green.
Statistical analysis of social science problems, primarily
drawn from political science and psychology, presented with a unified
foundation in basic statistical theory.
Statistics 102aG - EP & E 203a - Political Science 425a (66102-03) Introduction to Statistics: Data Analysis.
Instructor: Mr. Andrew Barron, Mr. John Hartigan.
An introduction to probability and statistics with emphasis on
analysis of data, presented with a unified foundation in basic
statistical theory.
Statistics 200La and 200Lb (66200), Statistical Computing Laboratory
Instructor: Mr. D. Pollard (fall) Mr. J. Hartigan (spring). Friday 2:30pm - 5pm
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 230a, 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.
At Stat Lab, 140 Prospect.
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Statistics 230a (66230), Introductory Data Analysis (FALL)
Cross-listing: Statistics 530a, PLSC 530b
Instructor: Mr. N. Hengartner. MW 2:30 pm - 3:45 pm
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. Concurrent with
Statistics 200L; after or concurrent with
Statistics 101a.
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Statistics 241a (66241), Probability Theory (FALL)
Cross-listing: Statistics/Mathematics 541a
Instructor: Mr. D. Pollard. MWF 9:30 am - 10:20 am
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.
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Statistics 242b (66242), Theory of Statistics (SPRING)
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 (SPRING)
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.
Statistics 312a (66312), Linear Models (FALL)
Cross-listing: Statistics 612a
Instructor: Mr. D. Pollard. 10:30 am - 11:20 am
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.
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Statistics 361b (66361), Data Analysis (SPRING)
Cross-listing: Statistics 661b
Instructor: Mr. J. Hartigan.
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 242 or
equivalent. Statistics 200L is
a prerequisite.
Statistics 364b (66364), Information Theory (SPRING)
Cross-listing: Statistics 664b
Instructor: Mr. J. Chang.
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, redunda ncy, mutual information, channel
capacity. Basic theorems of data compression, data summarization, and
channel coding. Applications in statistics and finance. After statistics
Statistics 241.
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Statistics 600b (66600), Advanced Probability (SPRING)
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.
Statistics 603b (66603), Stochastic Calculus (SPRING)
Instructor: Mr. J. Chang.
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 coutnin
g 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, will be reviewed.
After: Statistics 600.
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Statistics 610a (66610), Statistical Inference (FALL)
Instructor: Mr. M. Wegkamp.
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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Statistics 625a (66625), Statistical Case Studies (FALL)
Instructor: Mr. J. Hartigan.
We will study large data sets on second hand smoke, reticulate
evolution, bloc voting, NCAA Academic Thresholds, Connecticut
Educational Standards - and other fun things.
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Statistics 626b (66626), Practical Work (SPRING)
Instructor: Mr. J. Hartigan.
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 665a (66665), Function Estimation (FALL)
Instructor: Mr. N. Hengartner.
Nonparametric function estimation techniques are important tools for modern data analysis. In this course, we will study and compare methodologies, such as kernel based methods, regression splines, neural networks, hazard functions, conditional expec
tations and conditional medians. Further topics covered include data driven bandwidth selection, adaptive estimation, local likelihoods, additive models and uniform confidence bands. I expect the students to have had introductory courses in probability an
d statistics, at least at the Stat 241-242 level.
Time: Times to be arranged at organizational meeting.
Statistics 666b (66666), Resampling (SPRING)
Instructor: Mr. M. Wegkamp.
The "bootstrap" and "jackknife" are popular computer intensive methods
in Statistics. Typically they improve upon more traditional methods for
estimating the variability of statistical quantities. We shall be
concerned with theoretical questions like: "When does the bootstrap
work?" and "Why are they more accurate?" etc. Some keywords:
consistency, second order correctness, bootstrapping in finite populations,
density estimation, M-estimation, empirical processes, statistical
functionals.
Statistics 670b (66670), Time Series (SPRING)
Instructor: Mr. A. Barron.
Characterizing and extracting components of signals, methods of prediction, statitical models and inference in time series. Students are expected to find and analyze time series and to complete and present reports.
Time: Times to be arranged at organizational meeting
Statistics 685a (66685), Asymptotic Admissibility (FALL)
Instructor: Mr. J. Hartigan.
This course will develop a general method for evaluating
estimation procedures. It will develop methods for doing Edgeworth
expansions in many dimensions which will be applied to posterior
distributions and to distributions of estimates. In this way, the
asymptotic risks of Bayes estimates and other estimates will be
computed. In particular it will be shown under which conditions
maximum likelihood estimates are asymptotically Bayes and so
asymptotically admissible.
Time: Times to be arranged at organizational meeting
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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-