Transcript Modeling in the presence of uncertainty: Jennifer Hoeting

Modeling in the presence of
uncertainty:
Is there another way to do it?
Jennifer Hoeting
Colorado State University
• New conference title: “The movers and
shakers of ecological modeling”
• A big thank you to the workshop
organizers: Noel, Jim, Jay, Kate, Chris;
and the faculty and staff of MBI
• … even if this conference did force a
University of Michigan alumna to attend a
function at archrival Ohio State – Go Blue!
Two Themes for Discussion
1. Uncertainty
2. Graduate Education
Uncertainty
• Are we doing enough (yet) to convey
uncertainty in analyses of ecological
problems?
• Does too much focus on uncertainty
distract from the main point of our
analyses (inference)?
Uncertainty: Is there a best method
for model selection/specification?
• Bayesian (e.g., Gelfand): use MANY
parameters and let the data speak for
themselves
• Semi-parametric (e.g., Breidt): combine
model-based and design-based inference
• Likelihood (e.g., Burnham): AIC is taking
the world of ecology by storm. Is this “new
religion” the right direction for ecology?
Uncertainty: Is there a best method
for model selection/specification?
• Does our choice of model or analysis
depend on whether the data are from a
probability-based sample (Breidt) or an
observational study (Gelfand and Breidt)?
• Are covariates really “known covariates”?
Graduate Education
• Are we equipping our graduate students
with the right tools to become the next
Cressie, Gelfand, Clark, Hastings, or
insert your name here?
• How much ecology do I need to know?
Graduate Education
• If the focus is to train statisticians who will
impact ecology, what is the right blend of
traditional and modern statistical methods
in graduate study?
• Multi-disciplinary is all the rage in
academia. Does an interdisciplinary
degree water down a statistics or ecology
degree?