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:
|
303
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistics and the Environment
|
Abstract - #304982 |
Title:
|
Nonparametric Inference in Small Data Sets of Spatially Indexed Curves with Application to Ionospheric Trend Determination
|
Author(s):
|
Sasha Gromenko*+ and Piotr Stefan Kokoszka
|
Companies:
|
Utah State University and Colorado State University
|
Address:
|
, , ,
|
Keywords:
|
Functional data ;
Spatial Statistics ;
Ionospheric trends ;
Nonparametric inference
|
Abstract:
|
This research is concerned with estimation and testing in data sets consisting of a small number (about 20--30) of curves observed at unevenly distributed spatial locations. Such data structures may be referred to as spatially indexed functional data. Motivated by an important space physics problem, we model such data as a mean function plus spatially dependent error functions. Given a small number of spatial locations, the parametric methods for the estimation of the spatial covariance structure of the error functions are not satisfactory. We propose a fully nonparametric estimator for the mean function. We also derive a test to determine the significance of the regression coefficients if the mean function is a linear combination of known covariate functions. In particular, we develop methodology for the estimation a trend in spatially indexed functional data, and for assessing its statistical significance. We apply the new tools to global ionosonde records to test the hypothesis of ionospheric cooling. Nonparametric modeling of the space-time covariances is surprisingly simple, much faster than those previously proposed, and less sensitive to computational errors.
|
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.