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A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_04C | Sat, 8/2/2014, 8:30 AM - 5:00 PM | CC-161 | |
| Hierarchical Bayesian Modeling and Analysis for Spatial Data — Professional Development Continuing Education Course | |||
| ASA , Section on Bayesian Statistical Science | |||
| In this course we will describe hierarchical modeling and related Markov chain Monte Carlo (MCMC) methods for spatial statistics. We will begin by outlining and providing illustrative examples of the three types of spatial data: point-level (geostatistical), areal (lattice), and spatial point patterns. We then describe both exploratory data analysis tools and traditional modeling approaches for point-referenced data. Since our approach is fully model-based through the use of Gaussian processes, we develop the basics of spatial Gaussian process models. Approaches from traditional geostatistics (variogram fitting, kriging, etc.) will be briefly covered here. We then turn to areal data models, again starting with choropleth maps and other displays and progressing towards more formal model specifications, e.g., Markov random fields that underlie the conditional, intrinsic, and simultaneous autoregressive (CAR, IAR, and SAR) models widely used in areal data settings. The remainder of our presentation will cover hierarchical modeling for both univariate and multivariate spatial response data, including Bayesian kriging and lattice modeling, as well as more advanced issues such as anisotropy and nonstationarity. We also include a discussion of spatial point process models, and modern computational approaches for very large data sets (the so-called ``big N problem"). Short course participants should have an M.S. understanding of mathematical statistics at, say, the Hogg/Craig/Tanis or Casella/Berger levels, as well as basic familiarity with Bayesian modeling and computing at the Carlin/Louis or Gelman et al. levels. We will not assume any significant previous exposure to spatial or spatiotemporal methods. | |||
| Instructor(s): Sudipto Banerjee, University of Minnesota, Bradley P. Carlin, University of Minnesota, Alan Gelfand, Duke University | |||
2014 JSM Online Program Home
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