Inference for Spatial Processes Using Subsampling: a Simulation Study

Many environmental studies involve the measurement of ecological indices that yield spatially dependent data. One quantity that captures the empirical distribution of ecological measurements is the spatial cumulative distribution function (SCDF). Methods for making inferential statements about SCDFs...

Full description

Saved in:
Bibliographic Details
Published in:Environmetrics (London, Ont.) Vol. 8; no. 5; pp. 485 - 502
Main Authors: Kaiser, Mark S., Hsu, Nan-Jung, Cressie, Noel, Lahiri, Soumendra N.
Format: Journal Article Conference Proceeding
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01.09.1997
Wiley
Subjects:
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Exact sciences and technology
Pollution, environment geology
prediction region
spatial cumulative distribution function
spatial dependence
prediction
environment protection
digital simulation
spatial variations
statistical analysis
spatial distribution
ISSN:1180-4009, 1099-095X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many environmental studies involve the measurement of ecological indices that yield spatially dependent data. One quantity that captures the empirical distribution of ecological measurements is the spatial cumulative distribution function (SCDF). Methods for making inferential statements about SCDFs have only recently been developed, one being that of spatial subsampling. While spatial subsampling produces inferential quantities with known asymptotic properties, the performance of this methodology in a finite‐sample setting has not previously been investigated. In this article, we review the subsampling method and its theoretical justification, and investigate the performance of this method for finite samples with a simulation study involving several subsampling designs and types of spatial dependence. The subsampling methodology appears to give quite good results over a range of realistic spatial processes. For application to a set of spatially dependent data, an appropriate subsampling procedure may be designed on the basis of quantities contained in the (estimated) variogram. © 1997 John Wiley & Sons, Ltd.
Bibliography:ArticleID:ENV271
istex:E85435AFBF763649F02A6A0BAF356E6084FF308E
ark:/67375/WNG-40GQHS0S-5
ISSN:1180-4009
1099-095X
DOI:10.1002/(SICI)1099-095X(199709/10)8:5<485::AID-ENV271>3.0.CO;2-3