The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
Activity Number:
|
410
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistics and the Environment
|
Abstract - #304153 |
Title:
|
Spatio-Temporal Models for Some Data Sets in Continuous Space and Discrete Time
|
Author(s):
|
Samuel Demel*+ and Juan Du
|
Companies:
|
Kansas State University and Kansas State University
|
Address:
|
Department of Statistics, Manhattan, KS, 66506, United States
|
Keywords:
|
Autoregressive and moving average process (ARMA) ;
Covariance ;
Spatio-temporal covariance function ;
Spatio-temporal covariance function ;
Spatial statistics ;
Fourier transform
|
Abstract:
|
Space time data sets are often collected at monitored discrete time lags, which are normally viewed as a component of time series. Valid and practical covariance structures are needed to model these types of data sets in various disciplines, such as environmental science, climatology and agriculture. In this work we propose two classes of spatio-temporal functions whose discrete temporal margins are some celebrated autoregressive and moving average (ARMA) models, and obtain necessary and sufficient conditions for them to be spatio-temporal covariance functions. The possibility of taking the advantage of well-established time series and spatial statistics tools makes it relatively easy to identify and fit the proposed model in practice. Simulation study is conducted to illustrate the application of the proposed model for estimation or prediction, comparing with some general existing parametric models in terms of likelihood and mean squared prediction error. A spatio-temporal model with AR(2) discrete temporal margin is fitted to wind data from Ireland
|
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 2012 program
|
2012 JSM Online Program Home
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.