JSM 2005 Online Program

JSM Activity #CE_16C

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter


		Add To My Program
CE_16C	Mon, 8/8/05, 8:15 AM - 4:15 PM	MCC-L100 A
Hierarchical Modeling and Analysis for Spatial Data - Continuing Education - Course
ASA, Section on Bayesian Statistical Science
Instructor(s): Bradley P. Carlin, University of Minnesota, Sudipto Banerjee, University of Minnesota, Alan E. Gelfand, Duke University
This course will describe hierarchical modeling methods for spatially oriented data. We will begin by outlining and providing illustrative examples of the three types of spatial data: point-level (geostatistical), areal (lattice), and spatial point process. We then describe both exploratory data analysis tools and traditional modeling approaches for point-referenced data. Modeling approaches from traditional geostatistics (variogram fitting, kriging, etc.) will be covered here. We shall then offer a similar presentation for areal data models, again starting with choropleth maps and other displays, and progressing towards more formal statistical modeling. 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 spatially varying coefficient, spatio-temporal, and spatial survival models. Short course participants should have an M.S. understanding of mathematical statistics, as well as basic familiarity with standard statistical models and computing. We will not assume significant previous exposure to spatial or Bayesian methods, although students with basic knowledge of these areas (say, based on the books by Cressie, 1993, and Carlin and Louis, 2000, respectively) will face a gentler learning curve. OPTIONAL TEXTBOOK AVAILABLE