Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies

Environmental studies often employ data collected over large spatial regions. Although it is convenient, the conventional single model approach may fail to accurately describe the relationships between variables. Two alternative modeling approaches are available: one applies separate models for diff...

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Main Author: Zhang, Huizi
Other Authors: Statistics
Format: Others
Published: Virginia Tech 2014
Subjects:
Online Access:http://hdl.handle.net/10919/28721
http://scholar.lib.vt.edu/theses/available/etd-08192006-115350/
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spelling ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-287212020-09-26T05:34:13Z Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies Zhang, Huizi Statistics Smith, Eric P. Ye, Keying Boone, Edward Prins, Samantha C. Bates Environmental studies Clustering Hierarchical Model Classification Environmental studies often employ data collected over large spatial regions. Although it is convenient, the conventional single model approach may fail to accurately describe the relationships between variables. Two alternative modeling approaches are available: one applies separate models for different regions; the other applies hierarchical models. The separate modeling approach has two major difficulties: first, we often do not know the underlying clustering structure of the entire data; second, it usually ignores possible dependence among clusters. To deal with the first problem, we propose a model-based clustering method to partition the entire data into subgroups according to the empirical relationships between the response and the predictors. To deal with the second, we propose Bayesian hierarchical models. We illustrate the use of the Bayesian hierarchical model under two situations. First, we apply the hierarchical model based on the empirical clustering structure. Second, we integrate the model-based clustering result to help determine the clustering structure used in the hierarchical model. The nature of the problem is classification since the response is categorical rather than continuous and logistic regression models are used to model the relationship between variables. Ph. D. 2014-03-14T20:15:25Z 2014-03-14T20:15:25Z 2006-08-14 2006-08-19 2006-09-29 2006-09-29 Dissertation etd-08192006-115350 http://hdl.handle.net/10919/28721 http://scholar.lib.vt.edu/theses/available/etd-08192006-115350/ dissertation.pdf In Copyright http://rightsstatements.org/vocab/InC/1.0/ application/pdf Virginia Tech
collection NDLTD
format Others
sources NDLTD
topic Environmental studies
Clustering
Hierarchical Model
Classification
spellingShingle Environmental studies
Clustering
Hierarchical Model
Classification
Zhang, Huizi
Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
description Environmental studies often employ data collected over large spatial regions. Although it is convenient, the conventional single model approach may fail to accurately describe the relationships between variables. Two alternative modeling approaches are available: one applies separate models for different regions; the other applies hierarchical models. The separate modeling approach has two major difficulties: first, we often do not know the underlying clustering structure of the entire data; second, it usually ignores possible dependence among clusters. To deal with the first problem, we propose a model-based clustering method to partition the entire data into subgroups according to the empirical relationships between the response and the predictors. To deal with the second, we propose Bayesian hierarchical models. We illustrate the use of the Bayesian hierarchical model under two situations. First, we apply the hierarchical model based on the empirical clustering structure. Second, we integrate the model-based clustering result to help determine the clustering structure used in the hierarchical model. The nature of the problem is classification since the response is categorical rather than continuous and logistic regression models are used to model the relationship between variables. === Ph. D.
author2 Statistics
author_facet Statistics
Zhang, Huizi
author Zhang, Huizi
author_sort Zhang, Huizi
title Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
title_short Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
title_full Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
title_fullStr Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
title_full_unstemmed Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
title_sort classification analysis for environmental monitoring: combining information across multiple studies
publisher Virginia Tech
publishDate 2014
url http://hdl.handle.net/10919/28721
http://scholar.lib.vt.edu/theses/available/etd-08192006-115350/
work_keys_str_mv AT zhanghuizi classificationanalysisforenvironmentalmonitoringcombininginformationacrossmultiplestudies
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