|
Activity Number:
|
137
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 4, 2008 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
| Abstract - #301899 |
|
Title:
|
The Generalized Linear Model with Spatial Variation and Multiple Covariates
|
|
Author(s):
|
J. Aleong*+
|
|
Companies:
|
University of Vermont
|
|
Address:
|
Dept of Mathematics and Statistics, Burlington, VT, 05405,
|
|
Keywords:
|
Hierarchical generalized linear model ; Spatial statistics ; Longitudinal Study, Correlation, Model Checking.
|
|
Abstract:
|
The generalized linear models (GLM) and quasi-likelihood give a flexible framework for analyzing data generated from an exponential family of distributions which includes non-normal data with many types of correlation structures. This theory includes the analysis of discrete and categorical spatial data. Treatment effects in a designed experiment involving continuous and discrete spatial responses with covariates can be estimated and tested. Universal kriging and indicator kriging are discussed as special cases. Testing model assumptions to discriminate between correlation structures will be demonstrated. Examples are given, on comparing treatments in a designed experiment in which spatial correlation is present.
|
- 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 2008 program |
