JSM 2005 Online Program

Abstract #302629

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:	337
Type:	Invited
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	ENAR
Abstract - #302629
Title:	Estimating Space-time Trends Combining Stochastic Models and Numerical Models
Author(s):	Montserrat Fuentes*+
Companies:	North Carolina State University
Address:	Statistics Department, Raleigh, NC, 27695,
Keywords:	covariance ; data assimilation ; spatial modeling ; environmental statistics
Abstract:	Estimating spatial temporal trends of air pollution levels is vital for air quality management and presents statistical problems typical of many environmental and spatial applications. Ideally, such trends would be based on a dense network of monitoring stations, but this does not always exist. Instead, there are generally two main sources of information about pollution levels: one is pollution measurements at a relatively sparse set of monitoring stations and the other is the output of the regional scale air quality models. Here, we develop formal methods for combining sources of information with different spatial resolutions for space-time trend estimation. We formulate this problem using a hierarchical model in which scientific information and output of numerical models is introduced to improve the trend estimation. We also offer a review of the current literature and approaches that use output of numerical models as a prior for trend estimation. We apply these methods to estimate the risk of mortality associated to fine particulate matter using air quality information from numerical models and monitoring stations.

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