course | number | instructor | level | time |
---|---|---|---|---|
Introduction to Statistics | 101-106a | Chang et al | intro, no prereqs | T,Th 1:00-2:15 |
Probability Theory | 241/541a | Wegkamp | intro, with calculus | M,W,F 9:30-10:20 |
Linear Models | 312/612a | Pollard | intermediate | T,Th 9:00-10:15 |
Analysis of Spatial & Time Series Data | 374/664a | Radulovic | T,Th 1:00-2:15 | |
Statistical Inference | 610a | Barron | intro grad | M, W 1:00-2:20 |
Asymptotic Theory | 618a | Wegkamp | T,Th 10:30-11:45 | |
Statistical Case Studies | 625a | Reuning-Scherer | intermediate grad | M 1:00-3:30 |
Some Topics in Portfolio Selection | 676a | Barron | T,Th 2:30-3:45 | |
Internship in Statistical Research | 695a | Wegkamp | ||
Introductory Data Analysis | 230/530b | Pollard | intro | M,W 2:30-3:45 |
Theory of Statistics | 242/542b | Emerson | intro, with calculus | M,W,F 9:30-10:20 |
Stochastic Processes | 251/551b | Radulovic | intermediate | M,W 1:00-2:15 |
Advanced Probability | 330/600b | Pollard | adv. undergrad/
intermediate grad |
T,Th 2:30-3:45 |
Data Analysis | 361/661b | Hartigan | intermediate | M,W 2:30-3:45 |
Information Theory | 364/664b | Yeh | intermediate | T,Th 9:00-10:15 |
Intro. to Function Estimation | 365/665b | Wegkamp | M,W 11:30-12:45 | |
Central Limit Theorem | 602b | Radulovic | T,Th 1:00-2:15 | |
Applied Math Senior Seminar | AM490b | Chang | W 3:30 - 5:20 | |
Practical Work | 626b | Wegkamp | adv. grad | contact instructor |
Statistical Methods in Genetics and Bioinformatics | 645b | Chang | T,Th 4:00-5:15 | |
Bayes Theory | 653b | Hartigan | T,Th 10:30-11:45 | |
Multivariate Statistics for Environmental and Social Sciences | 660b | Reuning-Scherer | M,W 1:00-2:20 |
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:
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 210aG / MCDB 215a, Introduction
to Statistics: Life Sciences.
Instructor: Mr. Joseph Chang/ Mr. (in charge).
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/Ms. Rose Razaghian
(in charge).
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 104a / PSYC 201a, Introduction to Statistics:
Psychology.
Instructor: Mr. Joseph Chang/Mr. Thomas Brown
(in charge).
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, Introduction to Statistics:
Medicine.
Instructor: Mr. Joseph Chang/Mr. Marek
Chawarski (in charge).
Statistical methods relied upon in medicine and
medical research. Practice in reading medical literature competently
and critically, as well as practical experience performing statistical
analysis of medical data.
STAT 106a, Introduction to Statistics: Data
Analysis.
Instructor: Mr. Joseph Chang/Ms. Mihaela
Aslan (in charge).
An introduction to probability and statistics
with emphasis on data analysis.
[MORE COURSE INFORMATION]
STAT 241a, Probability
Theory (FALL)
Cross-listing: Statistics 541a
Instructor: Mr. Marten Wegkamp
Time: Mon, Wed, Fri 9:30 - 10:20
Place: LOM 202
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.
[MORE COURSE INFORMATION]
STAT 242b,
Theory of Statistics (SPRING)
Cross-listing: Statistics 542b, Mathematics
242b
Instructor: Mr. John Emerson
Time: Mon, Wed, Fri 9:30 - 10:20
Place: BCT 102
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.
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COURSE INFORMATION]
STAT 251b,
Stochastic Processes (SPRING)
Cross-listing: Statistics 551b
Instructor: Mr. Dragan Radulovic
Time: Mon, Wed 1 - 2:15
Place: BCT C031
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.
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COURSE INFORMATION]
STAT 312a, Linear
Models (FALL)
Cross-listing: Statistics 612a
Instructor: Mr. David Pollard
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.
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COURSE INFORMATION]
STAT 361b,
Data Analysis (SPRING)
Cross-listing: Statistics 661b
Instructor: Mr. John Hartigan
Time: Mon, Wed 2:30 - 3:45
Place: Dunham 120
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. After
Statistics
242 and Mathematics 222b or 225a or b, or equivalents.
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COURSE INFORMATION]
STAT 364b,
Information Theory (SPRING)
Cross-listing: Statistics 664b
Instructor: Mr. Edmund Yeh
Time: Tue, Thu 9:00 - 10:15
Place: 24 Hillhouse Avenue, 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.
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COURSE INFORMATION]
STAT 365b,
Introduction to Function Estimation (SPRING)
Cross-listing: Statistics 665b
Instructor: Mr. Marten Wegkamp
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.
[MORE
COURSE INFORMATION]
STAT 374a, Analysis
of Spatial and Time Series Data (FALL)
Cross-listing: Statistics 674a
Instructor: Mr. Dragan Radulovic
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.
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COURSE INFORMATION]
AM490b,
Applied Math Senior Seminar and Project (SPRING)
Cross-listing:
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.
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COURSE INFORMATION]
STAT 600b,
Advanced Probability (SPRING)
Cross-listing: Statistics 330b
Instructor: Mr. David Pollard
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.
[MORE
COURSE INFORMATION]
STAT 602b,
Central Limit Theorem (SPRING)
Instructor: Mr. Dragan Radulovic
Time: Tues, Thurs 1:00 pm - 2:15 pm
Place: 24 Hillhouse Avenue, Room 107
Central limit theorem (CLT) plays a key role
in numerous statistical applications and it has imbedded itself in many
theoretical models. The proposed topics course would cover (besides the
historical accounts and the obvious "standard" CLT) the following topics:
The "infinite variance case" (P-stable limits, infinite divisible laws
and Poisson(mu) limits), "dependence case" (alpha and beta mixing, CLT
for time series and Markov chains), "multi dimensional extension" (Empirical
processes, Banach space valued random variables) and the Bootstrap for
the above. Each of the above topics will be motivated by real life problems.
Although no specific prerequisite courses are required the knowledge of
measure-theoretical probability. (Statistics 600) is strongly encouraged.
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COURSE INFORMATION]
STAT 610a, Statistical
Inference (FALL)
Instructor: Mr. Andrew Barron
Time: Mon, Wed 1:00 pm - 2:15 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.
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COURSE INFORMATION]
STAT 618a, Asymptotic
Theory (FALL)
Instructor: Mr. Marten Wegkamp
Time: Tues, Thurs 10:30 am - 11:45 am
Place: 24 Hillhouse Avenue, Room 107
A careful introduction to asymptotic methods
in mathematical statistics. Topics include: Consistency and
Asymptotic Distributions, Edgeworth Expansions, M-estimators, Contiguity,
Local Asymptotic Normality, Efficiency, Likelihood Ratio Theory, Le Cam's
Theory for Convergence of Experiments, Bootstrap. After Statistics
600b and Statistics 610b.
[MORE
COURSE INFORMATION]
STAT 625a, Statistical
Case Studies (FALL)
Instructor: Mr. Jonathan Reuning-Scherer.
Time: Mon 1:00 pm - 3:30 pm
Place: 24 Hillhouse Avenue, Room B6
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.
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COURSE INFORMATION]
STAT 626b,
Practical Work (SPRING)
Instructor: Mr. Marten Wegkamp/Staff.
Individual one-semester projects, with students
working on studies outside the Department, under the guidance of a statistician.
[MORE
COURSE INFORMATION]
STAT 645b,
Statistical Methods in Genetics and Bioinformatics (SPRING)
Instructor: Mr. Joseph Chang
Time: Tues, Thurs 4:00 pm - 5:15 pm
Place: 24 Hillhouse Avenue, Room 107
Stochastic modeling and statistical methods applied
to problems such as mapping quantitative trait loci, analyzing gene expression
data, sequence alignment, and reconstructing evolutionary trees. Statistical
methods include maximum likelihood, Bayesian inference, Monte Carlo Markov
chains, and some methods of classification and clustering. Models introduced
include variance components, hidden Markov models, Bayesian networks, and
coalescent. Recommended background: Stat 541, Stat 542. Prior knowledge
of biology is not required.
[MORE
COURSE INFORMATION]
STAT 653b,
Bayes Theory (SPRING)
Instructor: Mr. John Hartigan
Time: Tues, Thurs 10:30 am - 11:45 am
Place: 24 Hillhouse Avenue, Room 107
Axioms and interpretations of probability. Construction
of probability distributions. Optimality of Bayes procedures. Martingales.
Asymptotics. Markov Sampling. Robustness against violations in the assumed
distributions. Choice among models.
[MORE
COURSE INFORMATION]
STAT 676a, Some
Topics in Portfolio Selection (FALL)
Instructor: Mr. Andrew Barron
Time: Tues, Thurs 2:30 pm - 3:45 pm
Place: 24 Hillhouse Avenue, Room 107
A study of distributional properties of compounded
wealth in repeated gambling and in stock market investment. Wealth concentration
inequalities. Strategies of highest concentrated wealth. Normal theory
for log-wealth. Relationship to maximum likelihood theory in statistics
and to the asymptotic equipartition property in physics and information
theory. Greedy strategies. Universal portfolios and their relationship
to Bayes methodology. The ratio of idealized wealth (best with hindsight)
to actual wealth and the properties of this ratio, both for stochastic
stock price sequences and its minimax behavior for arbitrary price sequences.
Fast algorithms for universal portfolios.
[MORE
COURSE INFORMATION]
STAT 695a, Internship
in Statistical Research (1 credit) (FALL)
Instructor: Mr. Marten Wegkamp
The Internship is designed to give students an
opportunity to gain practical exposure to problems in the analysis of statistical
data, as part of a research group within industries such as: medical and
pharmaceutical research, financial, information technologies, telecommunications,
public policy, and others. The Internship experience often serves
as a basis for the Ph.D. dissertation. Students will work with the
Director of Graduate Studies and other faculty advisors to select suitable
placements.
Students will submit a one-page description of
their Internship plans to the DGS by May 1st, which will be evaluated by
the DGS and other faculty advisors by May 15th. Upon completion of
the Internship, students shall submit a written report of their work to
the DGS, no later than October 1st. The Internship will be graded
on a Satisfactory/ Unsatisfactory basis, and will be based on the student's
written report and an oral presentation.
This course is an elective requirement for the
Ph.D. degree.
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COURSE INFORMATION]
STAT 660b, Multivariate Statistics
for Environmental and Social Sciences (SPRING)
Cross-listing: FES 844b
Instructor: Mr. Jonathan Reuning-Scherer
Time: Mon, Wed 1:00 pm - 2:20 pm
Place: ESC 110 (21 Sachem Street)
An introduction to the analysis of multivariate
data. Topics to include multivariate analysis of variance (MANOVA),
principle components analysis, cluster analysis (hierarchical clustering,
k-means), canonical correlation,multidimensional scaling, and factor analysis.
Some analysis of multivariate spatial data may be included. Emphasis
is placed on practical application of multivariate techniques to a variety
of examples in the natural and social sciences. Students will be
required to select a dataset
early in the term for use throughout the semester.
There are regular assignments and a final project.
A complete syllabus is available on the classes
server.
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COURSE INFORMATION]
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.