Hidden Markov Random Fields: Spatial Random Effects for Lattice Data
|Date:||Friday, September 19|
|Time:||9:00 am -- 10:00 am|
In the statistical modeling of an environmental process, it can be
useful to decompose the process into large-scale and small-scale
structure, although there can be a number of ways to accomplish this. In
many cases the small-scale structure should include spatial or temporal
dependence or both. In this work, the dependence is incorporated through
random effects with a conditional autoregressive (CAR) structure, and
the resulting model is a generalized linear mixed model (GLMM).
Pseudo-likelihood as well as Bayesian inference is addressed and two
particular examples in atmospheric science are illustrated.