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Introductory course: optimal design of experiments

Introductory course on The Optimal Design of Experiments

September 19, 20 & 21, 2011
Antwerp, Belgium



The target audience for the course is starting Ph.D. students and anyone
else who would like a primer on optimal design.
Prerequisites for the course
are knowledge of basic statistics and regression analysis. Familiarity with
classical design of experiments is not required. The course is not highly
mathematical and therefore accessible to a broad audience
.

The course will start with an intuitive introduction of the topic and
gradually builds up to more complicated situations. Examples for the course
will be taken from industry, marketing, chemistry, medicine, ... to show the
wide applicability of the optimal design techniques. The attention will not
be restricted to optimal design for linear regression models, but Bayesian
optimal design and minimax designs for nonlinear regression models will also
be discussed. The strengths and weaknesses of optimal design will be
illustrated, and some remedies to overcome some of the problems will be
given.

The venue for the course is in the Antwerp city center, Prinsstraat 10, room
P011. Registration for the course costs 150 euro for academics and 750 euro
for others. This fee includes course material and lunches on September 19
and 20. Registrants can arrange cheap accomodation in nearby hotels. The
Antwerp city center is easy to reach by train, and there is an hourly bus
service from and to Brussels National Airport.

For further information, please contact Peter Goos at
peter.goos@ua.ac.be


Lecturer: Peter Goos


Course contents:

In total, the course takes 2.5 days. The programme of the first 2 days is as
follows:

- Introduction to design of experiments

- Intuitive introduction to optimal design of experiments

- Optimal design for linear regression models ( first-order models,
second-order models, constrained design regions, algorithms)

- Optimal design for nonlinear regression models ( local optimal design,
Bayesian optimal design, minimax design, algorithms)

- Extensions to non-standard design problems ( blocked experiments, paired
comparison and choice experiments, experiments with hard-to-change factors
(split-plot experiments), model uncertainty)

The last half day is reserved for advanced topics like split-split-plot
designs, strip-plot designs, and computationally efficient methods for
Bayesian optimal design.

The course will take place in a computer class so that the course
participants can work on a few algorithms and examples themselves. Various
software packages are demonstrated as well.

Schedule: 19 & 20 September (9am-6pm), 21 September (9am-1pm)

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