Statistical methods for spatial screening

dc.contributor.advisor Max Morris
dc.contributor.author Zhou, Zhigang
dc.contributor.department Statistics
dc.date 2018-08-25T01:56:01.000
dc.date.accessioned 2020-06-30T08:03:40Z
dc.date.available 2020-06-30T08:03:40Z
dc.date.copyright Sat Jan 01 00:00:00 UTC 2005
dc.date.issued 2005-01-01
dc.description.abstract <p>Military bases that have been used for weapon-testing and training usually are contaminated with unexploded ordnance (UXO). These sites can be returned to public use only after UXO remediation. The cleaning-up procedure is usually very expensive and time-consuming. This demands statistical tools to provide more effective sampling strategy and to characterize the UXO distribution. Based on the physical characteristics of UXO deposition, we adopt a simplified Neyman-Scott process to model the UXO distribution. A line transect survey is used to collect data on one coordinate of individual object locations. Two-stage (global and local) sampling strategy is applied to screen the contaminated site. In the global sampling, the estimators of the cluster intensity, mean cluster size and cluster dispersion are provided. The theoretical variance estimators of all the cluster parameters are also given. Simulation studies show that all the parameter estimates perform well and their theoretical variance estimates are reasonably close to their corresponding sample variances. In the local sampling, an inclusion region for covering all the unobserved objects in a cluster is proposed. Its asymptotic coverage property is given and proved. Simulation studies show the actual coverage of the inclusion region is very close to the nominal level.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/1790/
dc.identifier.articleid 2789
dc.identifier.contextkey 6105427
dc.identifier.doi https://doi.org/10.31274/rtd-180813-15413
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/1790
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/71764
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/1790/r_3200477.pdf|||Fri Jan 14 21:30:48 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Statistics
dc.title Statistical methods for spatial screening
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
File
Original bundle
Now showing 1 - 1 of 1
Name:
r_3200477.pdf
Size:
1.72 MB
Format:
Adobe Portable Document Format
Description: