Annual Report 2005 - Fields Institute - University of Toronto

Workshop participants
Speakers:
Randy Berry (Northwestern)
Spectrum sharing games
George Kesidis (Penn State)
Coupled Kermack-McKendrick models for randomly scanning
and bandwith-saturating internet worms
Richard La (Maryland)
Providing guaranteed packet loss rate in wireless networks in
the presence of random interference
Jang-Won Lee (Princeton)
Implications to network utility through physical and medium
access layer innovations
Roland Malhame (Ecole Polytechnique Montreal)
Optimal scheduling of data transmission in wireless networks
Ravi Mazumdar (Waterloo)
Random mobility models in ad hoc networks: capacity and
delay issues
Michael Neely (Southern California)
Stochastic control of ad-hoc networks: delay, energy, fairness
Asuman Ozdaglar (MIT)
Competition and Efficiency in Communication Networks
Saswati Sarkar (Pennsylvania)
Stochastic control problems in MAC layer wireless multicast
G e n e r a l S c i e n t i f i c A c t i v i t i e s
Sanjay Shakkottai (Texas at Austin)
The price of anarchy in min-cost multicast with network coding
R. Srikant (Illinois at Urbana Champaign)
Scheduling in multihop wireless networks
K. S. Srisankar (Motorola)
QoS provisioning in wireless ad-hoc networks
Edmund Yeh (Yale)
Power control, rate allocation, and routing in stochastic wireless
networks
Workshop on Empirical Likelihood Methods
May 9–11, 2005
Held at the University of Ottawa
Organizers: Mayer Alvo (Ottawa) and Jon Rao (Carleton)
Inference based on likelihood methods has proven very
effective in the past. The methods can be used for constructing
tests with good asymptotic properties, confidence
intervals with accurate coverage probability as well as
to pool data from different sources. Unfortunately in
parametric statistics one must suppose that the underlying
family of distributions is known, save for some parameters
which are estimated from the data. The use of parametric
methods when the underlying distributions are unknown
can lead to incorrect tests and inaccurate confidence intervals.
Empirical Likelihood has been applied successfully in
determining confidence intervals whose shape and orientation
is dictated by the data. These intervals also respect the
range of the parameter space. The method has proven effective
in audit sampling where lower confidence bounds are
used to compute the amount of money owed to government
for example. The empirical confidence intervals outperformed
the parametric likelihood intervals by providing
large lower bounds respecting the nominal error rate.
Art Owens (Stanford) presented a tutorial on empirical
likelihood methods. This was followed by presentations
by Jon Rao, Jiahua Chen, Changbao Wu, P.K.Sen, Mayer
Alvo, Qunshu Ren and Yongson Quin. There was a good
exchange of ideas throughout the workshop, which will
undoubtedly translate into a vigorous research in this
growing field.
Fields Institute 2005 ANNUAL REPORT 77