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Professor Douglas Wiens
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Course
materials:
- Course information, assessment procedures,
etc.
- Schedule
of lectures, assignments and exams.
- Lecture
notes. These are constantly under revision.
Obtaining
and using the R package:
Assignments:
Exams:
Technical
Writing:
- The importance of
clarity of exposition, and of grammatical correctness, in technical
writing cannot be over-emphasized. The best way to determine if you
understand what you are doing is to try to write it down in a form that
another reader can understand. Some comments along these lines are
included with the course information; for
some helpful resources see "Writing Aids" on my homepage.
R examples worked in class: You should
find it useful to download and run these R scripts before the classes in
which they are to be discussed.
- cars (Lecture 1) Some preliminary regression
fits
- acetylene1 (Lecture 4) and acetylene 2 (Lecture 5) An analysis of
a dataset exhibiting extreme multicollinearity; ridge regression
- logistic (Lecture 6) Example of logistic
regression through wls
- outlier (Lecture 7) Simulated example of
the effect of one highly influential outlier
- puromycin (Lecture 8) Fitting a
Michaelis-Menten model to the puromycin data
- bod1 (Lecture 9) Likelihood regions and
profile plots for the BOD data
- lubricant (Lecture 10) Some methods of
obtaining starting values
- smoothing1 (Lecture 12) Smoothing
spline (and other) fits to the motorcycle data
- smoothing2 (Lecture 13) Kernel and
loess fitting
- rock (Lecture 14) GAM and projection pusuit
fitting on the 'rock' data set
- ppreg (Lecture 14) Simulated
projection pursuit example
- lasso (Lecture 15) Computing the lasso
- quantile regression (Lecture 15); see
the Koenker paper
- ordinary M-estimation
(Lectures 17, 18) Computing an Ordinary M-estimate with Huber's psi
function
- mineral (Lectures 20 -- 23) Various
regression fits to the 'mineral' dataset from Maronna, Martin & Yohai
- my GM estimator (Lecture 22)
My function to compute GM-estimates
Matlab code for robustness of design
Data
sets used in assignments:
- pcb (Assignment 1)
- bod2 (Assignment 2)
- steady state (Assignment 2)
- water (Assignment 3):
download water.dat and water.R to the same directory, and run
water.R. Check that all 20 cases have appeared.
Other:
- Influential Observations, High
Leverage Points, and Outliers in Linear Regression by Chatterjee &
Hadi
- A Note on Computing
Robust Regression Estimates Via Iteratively Reweighted Least Squares
by Street, Carroll & Ruppert
- On One-Step GM
Estimates and Stability of Inferences in Linear Regression by Simpson,
Ruppert & Carroll
- Small sample
corrections for LTS and MCD by Pison, Van Aelst & Willems
- High Breakdown-Point and High Efficiency Robust
Estimates for Regression by Yohai
- A Fast Algorithm for
S-Regression Estimates by Salibian-Barrera & Yohai
- A Basis for the Selection of a
Response Surface Design by Box & Draper
- Robustness
of Design by Wiens
Statistics resources at U of A:
- Several of our courses,
including this one, can be used to obtain accreditation as a
practising statistician in Canada.
Homepage
of Doug Wiens
Department
of Mathematical and Statistical Sciences Home Page