statistics colloquium
Event Type:
Colloquium
Speaker:
Candace Berrett
Event Date:
Friday, March 4, 2016 -
3:30pm to 5:00pm
Location:
SMLC120
Audience:
General PublicFaculty/StaffStudentsAlumni/Friends
Sponsor/s:
Stat group
Event Description:
Title: Bayesian Spatial Binary Classification
Abstract: In analyses of spatially-referenced data, researchers often have
one of two goals: to quantify relationships between a response variable and
covariates while accounting for residual spatial dependence or to predict
the value of a response variable at unobserved locations. In this second
case, when the response variable is categorical, prediction can be viewed
as a classification problem. Many classification methods either ignore
response-variable/covariate relationships and rely only on spatially
proximate observations for classification, or they ignore spatial
dependence and use only the covariates for classification. The Bayesian
spatial generalized linear (mixed) model offers a tool to accommodate both
spatial and covariate sources of information in classification problems.
In this talk, we formally define spatial classification rules based on
these models. We also take a close look at two of these models that have
been proposed in the literature, namely the probit versions of the spatial
generalized linear model (SGLM) and the Bayesian spatial generalized linear
mixed model (SGLMM). We describe the implications of the seemingly slight
differences between these models for spatial classification and explore the
issue of robustness to model misspecification through a simulation study.
We also provide an overview of alternatives to the SGLM/SGLMM-based
classifiers and illustrate the various methods using satellite-derived land
cover data from Southeast Asia.
