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a field, functioning at the periphery of mainstream statistical
work, and was particularly in need of inexpensive, high-speed
computation to enable tools vital for visualization. Fortunately,
this computing power arrived and, with it, the advent of geo- Welcome to ASA
graphic information systems (GIS) software to create effective
maps that tell a broad range of stories. However, GIS software
had an obvious limitation from a statistician’s perspective—it was
descriptive, but not formally inferential. A wonderful opportu-
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nity for stochastic modeling revealed itself, an opportunity with
few players at the time and a growing need in the applied com-
munity as more data with spatial content were being collected.
Here, again, there was more good fortune for me, because
advantages to hierarchical modeling for analyzing spatial data
became evident—the advantages we attach to working in gen-
eral in the Bayesian framework: fully model-based inference with
accurate assessment of uncertainty. Additionally, the customary
asymptotics, which are employed in time series, so-called expand-
ing domain asymptotics, may be inappropriate for spatial data
where infill asymptotics are perhaps more relevant. However,
that the latter asymptotics typically reveal information about
unknowns is bounded in terms of inference. In this regard, the
Bayesian framework provides exact inference, avoiding possibly
inappropriate asymptotics. Of course, the implied caveat is that
the data never overwhelm the prior; we must be more attentive
to prior sensitivity than in other areas.
Today, my good fortune persists through continuing research
relationships with talented former students. In particular,
Sudipto Banerjee and Brad Carlin, both at the University of
Minnesota, invaluably helped to shape my spatial data research
agenda. During my career, I also found wonderful interdisciplin-
ary collaborators. When I was at the University of Connecticut,
I worked seriously at building bridges with ecology and evolu-
tionary biology, yielding productive research relationships with
John Silander and Kent Holsinger. Moving to Duke University
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made things even better, as Duke is a naturally collaborative
ASA membership.
institution; research teams develop over the campus with strong
encouragement from the university. What program on campus
Visit the ASA
is more naturally interdisciplinary than statistics? Now at Duke,
I have productive relationships with researchers in the Nicholas
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School of the Environment, particularly Jim Clark and Marie
Lynn Miranda.
In summary, it seems my career has been one of ongoing good
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luck. To some extent, I believe this is true, but I also believe
-Access all your ASA journal, CIS, and
you can make your own luck. Scientific curiosity, receptivity to
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reading in other fields, availability for collaboration, willingness
to listen, and attention to effective communication all facilitate
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creating opportunities. In addition, developing the full toolkit—
and others
strong theoretical training, stochastic modeling expertise, mod-
ern computing skills, and data analysis experience—enables one
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to take advantage of these opportunities. Some of us work better
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with certain parts of the toolkit and enjoy certain parts more
than others; however, as statisticians in the 21st century, we can
each find our own way to contribute and, in that sense, we are
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all fortunate. n
SEPTEMBER 2008 AMSTAT NEWS 23
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