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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

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