JSM 2004 - Toronto

Abstract #300875

This is the preliminary program for the 2004 Joint Statistical Meetings in Toronto, Canada. 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, 2004); 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.

Back to main JSM 2004 Program page
Activity Number: 373
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300875
Title: A Spatio-temporal Framework for Modeling Ambient Particulate Matter Concentration Levels
Author(s): Catherine A. Calder*+
Companies: Ohio State University
Address: 1958 Neil Ave., Columbus, OH, 43210,
Keywords: dynamic process convolutions ; Bayesian ; latent variable ; multivariate ; fine particulate matter ; coarse particulate matter
Abstract:

Elevated levels of particulate matter (PM) in the ambient air have been shown to be associated with certain adverse human health effects. As a result, monitoring networks that track PM levels have been established across the country. Some of the older monitors measure PM less than 10um in diameter (PM10) while the newer monitors track PM less than 2.5um in diameter (PM2.5); it is now believed that this fine component of PM is more likely to be related to the negative health effects associated with PM. We propose a bivariate dynamic process convolution model for PM2.5 and PM10 concentrations. Our aim is to extract information about PM2.5 from PM10 monitor readings using a latent variable approach and to provide better space-time interpolations of PM2.5 concentrations compared to interpolations made using only PM2.5 monitoring information. We illustrate the approach using PM2.5 and PM10 readings taken across the state of Ohio in 2000.

  • 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 2004 program

JSM 2004 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2004