JSM 2005 Online Program

Abstract #302744

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

Activity Number:	48
Type:	Invited
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	WNAR
Abstract - #302744
Title:	Bayesian Models for Spatial Extremes
Author(s):	Uli Schneider and Douglas Nychka and Daniel Cooley and Eric Gilleland*+
Companies:	Geophysical Statistics Project, NCAR and National Center for Atmospheric Research and University of Colorado at Boulder and National Center for Atmospheric Research
Address:	PO Box 3000, Boulder, CO, 80307-3000,
Keywords:	spatial statistics ; bayesian statistics ; extreme value theory ; air quality
Abstract:	We investigate daily maximum, eight-hour average ground-level ozone levels (ppb) collected from 72 monitoring stations in and around North Carolina with regard to its extreme behavior and spatial dependence in the context of the new U.S. Environmental Protection Agency (EPA) National Ambient Air Quality Standard (NAAQS), which is based on a seasonal fourth-highest order statistic. To build a statistical model, we use the generalized Pareto distribution (GPD) within a threshold model along with a Bayesian approach to incorporate the spatial structure of the different stations. We also discuss posterior modes and Markov chain Monte Carlo (MCMC) approaches to make inference about parameter and uncertainty estimates. Finally, we mention how to model the apparent seasonal effects of ground-level ozone.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program