Statistics 123a, Introduction to Statistical Methods and Probabilistic
Reasoning
Cross-listing: Statistics 523a
Instructor: Mr. P. Everson.
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.
[SYLLABUS]
Time: Tue., Thu., 1:00-2:15; Lab session Monday
afternoon; begins Thursday, September 7.
Statistics 200Lb, Statistical Computing Laboratory
Instructor: Mr. J. Hartigan.
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, and is recommended for those
taking
Statistics 242b.
It will be a prerequisite in future years for
Statistics 312a and Statistics 361a.
[SYLLABUS]
[Course material]
Time: Wednesday 2.30-5.00, Stat Lab, 140 Prospect
Statistics 230b, Introductory Data Analysis
Cross-listing: Statistics 530b, PLSC 530b
Instructor: Mr. J. Hartigan.
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]
Time: Tuesday 2:30-2:45 at 107 Dana; Thursday 2:30-3:45 at Stat Lab, 140 Prospect
Statistics 241a, Probability Theory
Cross-listing: Statistics/Mathematics 541a
Instructor: Mr. N. Hengartner.
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., Fr.,
9:30-10:20; begins Wednesday, September 6.
Statistics 242b, Theory of Statistics
Cross-listing: Statistics 542b, Mathematics 242b
Instructor: Mr. D. Pollard.
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.
[SYLLABUS]
[Course material]
Time: Mon., Wed., Fri., 9:30-10:20 in room 207 WLH.
Statistics 251b, Stochastic Processes
Cross-listing: Statistics 551b
Instructor: Mr. J. Chang.
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.
[SYLLABUS]
Time: Mon., Wed., 2:30-3:45 in room 104 Mason Lab.
Statistics 312a, Linear Models
Cross-listing: Statistics 612a
Instructor: Mr. J. Hartigan.
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. (In future years,
Statistics 200Lb will also be a prerequisite.)
[SYLLABUS]
Time: Tue., Thu., 2:30-3:45; begins Thursday, 7 September.
Statistics 361a, Data Analysis
Cross-listing: Statistics 661a
Instructor: Mr. N. Hengartner.
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. (In future years Statistics 200Lb will
also be a prerequisite.)
[SYLLABUS]
Time: Mon., Wed., Fri., 2:30-3:20; begins
Thursday, 7 September.
Statistics 364b, Introduction to Information Theory
Cross-listing: Statistics 664b
Instructor: Mr. A. Barron
Information theory foundations in mathematical communications,
statistical inference, probability theory, statistical mechanics, 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 241a
and Mathematics 301a.
Time: Thursday 10:30-12:00 in room 107 Dana House.
Statistics 600b, Advanced Probability
Cross-listing: Statistics 330b
Instructor: Mr. J. Chang.
Measure theoretic probability, conditioning, laws of large numbers,
convergence in distribution, characteristic functions, central limit theorems,
martingales. Some knowledge of real analysis is assumed.
[SYLLABUS]
Time: Tue., Thu., 2:30-3:45 in room 113 WLH.
Statistics 603a, Stochastic Calculus
Instructor: Mr. D. Pollard.
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 counting processes and
finance. Knowledge of measure-theoretic probability at the level of
Statistics 600b
is a prerequisite for the course, although some key
concepts, such as conditioning, will be reviewed.
[SYLLABUS]
Time: Times to be arranged at organizational meeting
Statistics 610a, Statistical Inference
Instructor: Mr. D. Pollard.
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.
[SYLLABUS]
Time: Tue., Thu., 10:30-11:45; begins Thursday, 7 September.
Statistics 614b, Hierarchical Models
Instructor: Mr. P. Everson.
Hierarchical models make it possible to account properly for different
levels and sources of variation in complex data sets and may be used
to provide better inferences for individual units by borrowing
strength of information from the ensemble. Emphasis will be on
applications to real problems and on the implementation of methods in
S-plus. After Statistics 612a.
Time: Wednesday 10:00-12:00 in room 107 Dana House.
Statistics 625a, Statistical Case Studies
Instructor: Mr. P. Everson.
Thorough analysis of complex data sets using S-plus, with emphasis on
the balance between graphical techniques and formal inferential
procedures. Problems arising in health policy will be investigated.
Time: Times to be arranged at organizational meeting
Statistics 626b, Practical Work
Instructor: Mr. J. Hartigan.
Individual one-semester projects, with students working on studies
outside the Department, under the guidance of a statistician.
Statistics 651b, Counterexamples
Instructor: Mr. N. Hengartner.
Foundational issues of statistical inference and probability are
explored through counter- examples. The course will be held
seminar-like in that we will work through papers by Fisher, Basu,
Berger, and others. Students are expect to have had some exposure to
classical mathematical statistics. Topics covered will included the
likelihood principle, notion of Statistical information, sufficiency,
estimation in presence of nuisance parameters, pivot Statistics and
fiducial inference, ancillary statistics, partial likelihoods,
randomization.
Time: Thursday 10:30-12:30 in room 107 Dana House.
Statistics 668b, Computational Learning Theory and Statistics
Instructor: Mr. A. Barron
Valiant's model for computationally feasible inference problems.
Determination of NP-hard problems and computationally feasible
subproblems: satisfiability, set splitting, inference of multivariate
Boolean functions, inference of multivariate polynomial functions,
optimization of multi-unit neural networks. The roles of empirical
process theory and measures of complexity. Intended for graduate
students in Statistics, Computer Science, Mathematics, Electrical
Engineering and Economics. After Statistics 241a
or equivalent.
Time: Friday 10:30-12:30 in room 107 Dana House.
Statistics 687a, Stochastic Models of Evolution
Instructor: Mr. J. Chang.
Probabilistic and statistical study of evolution, with emphasis on
population genetics and phylogeny reconstruction. Fluctuations of gene
frequencies, genealogies and coalescent processes. Effects of
selection, recombination, geography and variable population
size. Methods of phylogeny reconstruction and their statistical
properties. Markov models, parsimony, distance methods, maximum
likelihood, site-to-site rate variation and dependence. Reliability
of estimated trees and alternatives to tree-like phylogenies.
Recommended background: Probability and Statistics at the level of
Statistics 241a and Statistics 242b.
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-