Hidden Markov Random Fields: Spatial Random Effects for Lattice Data
Sep 19, 2015 - 9:00 AM
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Hidden Markov Random Fields: Spatial Random Effects for Lattice Data
Date: | Friday, September 19 |
Time: | 9:00 am -- 10:00 am |
Place: | Sweeney 1123 |
Speaker: | Jon Hobbs |
Abstract:
Abstract:
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.