(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Mitzi returns to her alma mater to give a talk at joint meeting of the Ann Arbor useR and ASA Meetups:
Abstract
This case study shows how to efficiently encode and compute an intrinsic conditional autoregressive (ICAR) model in Stan. When data has a neighborhood structure, ICAR models provide spatial smoothing by averaging measurements of directly adjoining regions. The Besag, York, and Mollié (BYM) model is a Poisson generalized linear model (GLM) which includes both an ICAR component and an ordinary random-effects component for non-spatial heterogeneity. We compare two variants of the BYM model and fit two datasets taken from epidemiological studies of Scottish lip cancer (56 regions) and New York city pedestrian traffic deaths (700 regions).
It’s based on her Stan case study on ICAR models.
Registration
The event is open to the public. Here are the Meetup registration details.
The post Mitzi’s talk on spatial models in Ann Arbor, Thursday 5 April 2018 appeared first on Statistical Modeling, Causal Inference, and Social Science.
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