Annual Report 2005 - Fields Institute - University of Toronto
Annual Report 2005 - Fields Institute - University of Toronto
Annual Report 2005 - Fields Institute - University of Toronto
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– temporal point processes, set – indexed martingales, spatial<br />
generalizations <strong>of</strong> renewal processes and so on.<br />
There is a lot <strong>of</strong> enthusiasm about this initiative among<br />
forestry researchers including those in ecology and hydrology<br />
as well as in fire. They are open to all sorts <strong>of</strong> statistical<br />
and probabilistic ideas. This is evident from a subsequent<br />
electronic discussion group that has been established that<br />
includes over 40 subscribers. This has been instrumental<br />
in the preparation <strong>of</strong> a proposal for a full NPCDS project.<br />
In addition, a follow-up meeting will take place at the<br />
Banff International Research Station next spring and a<br />
promising proposal has been submitted to GEOIDE for<br />
matching funds. All <strong>of</strong> this activity in such a short space <strong>of</strong><br />
time speaks to the vibrant environment being created by<br />
this group and to the promise <strong>of</strong> significant advances at the<br />
interface <strong>of</strong> Forestry, Probability and Statistics.<br />
The Centre de recherches mathématiques hosted the joint<br />
NPCDS/SAMSI workshop on Latent Variable Models and<br />
Survey Data for Social and Health Sciences Research. The<br />
workshop was attended by about 65 participants, from<br />
Canada, the United States, and the United Kingdom.<br />
Although the majority <strong>of</strong> participants were statisticians<br />
from universities or government agencies, there was also<br />
a good representation from the social sciences and health<br />
sciences. The opening tutorial, by Anders Skrondal and<br />
Sophia Rabe-Hesketh was based on their recent book<br />
and covered a great deal <strong>of</strong> ground, including a thorough<br />
introduction to the authors’ framework for latent variable<br />
models, and a variety <strong>of</strong> interesting applications. It was very<br />
valuable for both experts and newcomers to latent variable<br />
modelling. The meeting was tied to the NPCDS Complex<br />
survey data project.<br />
The workshop provided an opportunity to showcase some<br />
<strong>of</strong> the accomplishments <strong>of</strong> the SAMSI theme year on latent<br />
variable models. A review and summary was provided by<br />
the organizer <strong>of</strong> the theme year, Ken Bollen <strong>of</strong> the <strong>University</strong><br />
<strong>of</strong> North Carolina. Some <strong>of</strong> the sessions included<br />
reports and discussions <strong>of</strong> the activity <strong>of</strong> the complex<br />
surveys working group, formed at the opening workshop<br />
at SAMSI in September 2004. The difficulties in adapting<br />
latent variable methods to complex survey data are perhaps<br />
best appreciated in the context <strong>of</strong> multi-level models. Chris<br />
Skinner <strong>of</strong> Southampton <strong>University</strong> provided an excellent<br />
overview <strong>of</strong> progress to date in this area. Under his leadership,<br />
the complex surveys working group had focused on<br />
weighting and estimation for multilevel models. Several<br />
papers at the workshop provided further insights into<br />
currently available estimation techniques and s<strong>of</strong>tware. A<br />
T h e N a t i o n a l P r o g r a m o n<br />
C o m p l e x D a t a S t r u c t u r e s<br />
very gratifying feature <strong>of</strong> the workshop was the high quality<br />
<strong>of</strong> the presentations, and <strong>of</strong> the analyses put forward<br />
in applications. Attempts will be made to organize more<br />
occasions for statisticians and social scientists to interact on<br />
methodological challenges.<br />
A project on DataMining is being vetted by NPCDS, following<br />
an inaugural workshop held at <strong>Fields</strong> October 28–30,<br />
2004 (see the detailed report later in this section). The idea<br />
for this meeting emerged from discussions <strong>Fields</strong> organized<br />
between Generation 5 and NPCDS. A follow-up meeting is<br />
planned for fall <strong>2005</strong>.<br />
In other news, an NPCDS project on the Design and Analysis<br />
<strong>of</strong> Computer Experiments for Complex Systems received<br />
final approval and is moving forward following a successful<br />
inaugural workshop held at BIRS, July13–17, 2004. NPCDS<br />
funding also supported various interactions with SAMSI,<br />
and allowed junior researchers to attend the August 5–6,<br />
2004 <strong>Fields</strong> workshop on Missing Data Problems (see the<br />
General Scientific Activity section for a description <strong>of</strong> this<br />
event). Inaugural workshops on Spatial-temporal models for<br />
Marine Ecological Systems and Longitudinal and Clustered<br />
Data Analysis take place August and October <strong>of</strong> <strong>2005</strong><br />
respectively. Please watch for announcements as student<br />
travel stipends are available.<br />
NPCDS Projects underway<br />
Statistical Methods for Complex Survey Data (inaugural<br />
workshop April 30–May 2, 2003 at CRM)<br />
Statistical Genomics (inaugural workshop September 3–5,<br />
2003 at <strong>Fields</strong>)<br />
Design and Analysis <strong>of</strong> Computer Experiments for<br />
Complex Systems (inaugural workshop July 13–17, 2004 at<br />
BIRS)<br />
Workshops<br />
Workshop on Missing Data Problems<br />
August 5–6, 2004<br />
Held at the <strong>Fields</strong> <strong>Institute</strong><br />
- see General Scientific Activity section for a description <strong>of</strong><br />
the event<br />
Data Mining Methodology and Applications Workshop<br />
October 28–30, 2004.<br />
Held at the <strong>Fields</strong> <strong>Institute</strong><br />
Organizers: Hugh Chipman (Acadia), Antonio Ciampi<br />
(McGill) and Michael Vainder (Generation 5)<br />
Data mining is the science <strong>of</strong> learning from large and complex<br />
data sets. Although it lies at the intersection <strong>of</strong> many<br />
<strong>Fields</strong> <strong>Institute</strong> <strong>2005</strong> ANNUAL REPORT 48