The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
249
|
Type:
|
Contributed
|
Date/Time:
|
Monday, August 1, 2011 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistics and the Environment
|
Abstract - #302785 |
Title:
|
Gaussian Subordination Models on a Lattice with Environmental Applications
|
Author(s):
|
Sucharita Ghosh*+
|
Companies:
|
Swiss Federal Research Institute WSL
|
Address:
|
Zuercherstrasse 111, Birmensdorf, International, CH-8903, Switzerland
|
Keywords:
|
Spatial data ;
Smoothing ;
Long-range dependence ;
Large deviation ;
Forestry ;
Ecology
|
Abstract:
|
Suppose that spatial observations Y(i,j) occur on a lattice (i,j), i= 1,...,n, j=1,2,.,m such that the data are Gaussian subordinated via a function G that is unknown and arbitrary except that it allows for a Hermite polynomial expansion. The advantage of this model is that it allows for non-Gaussianity of the data and that the shape of the underlying probability distribution may be location dependent. We consider various correlation types and in particular short memory and long-memory correlations and address two topics: (a) the nonparametric regression problem where the errors are Gaussian subordinated as described above and (b) the species count problem where the background process that is decisive of species occurrence is a Gaussian subordinated process. Generalization to the case when the data are irregularly spaced in space are also considered. We discuss asymptotic results and some real data applications from environmental monitoring.
|
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 2011 program
|
2011 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.