Team 3: statistical Multi-source Predictive
I
am at the point in my graduate student life at
which I am evaluating possible career paths,
models and error estimation in support of
and my experience as a student intern for
Crop Production Forecasts and estimates
NISS-NASS provided me with much insight into
criselda Toto, Worcester Polytechnic
what is in store for me. It made me realize that
Institute
research will be a major part of my career, what-
ever path I decide to take. This realization gave
me the enthusiasm to learn valuable research
skills, while the program set-up provided me with
the opportunity to develop them.
Our team’s project focused on building a statis-
critically, however, and to be more sensitive to the
tical predictive model that mimics the forecasting
pros and cons of each step we took. I believe these
process for crop production conducted by NASS.
are valuable lessons, not just in research, but in life.
This forecasting process involves multiple sources of
Learning how statistics is used in forecasting
information, including the opinions of agricultural
agricultural production also brought a new area
experts. Our goal was to incorporate the subjectivity
of interest to my list. Having been in academia for
in the process into the statistical model, making the
nearly my whole life, the experience I’ve had doing
process more objective.
research on real-life problems is limited. This expe-
As the junior members of the team, postdoctoral
rience opened a door for possible application in my
colleague Jay Wang and I were given the major task
dissertation. I also made valuable contacts and built
of working on the theoretical and computational
relationships with those involved in the project,
aspects of the statistical modeling. Our team men-
which is something I deeply appreciate. Overall,
tors—both from academia and government—guid-
being an intern at NISS was a wonderful learning
ed us, but our perseverance and patience were put
experience. I am grateful to have been part of this
to test, sometimes leading to frustration and misun-
research collaboration. n
derstanding. These trials trained us to think more
December 2009 AmstAt News 51
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