Type of Document	Dissertation
Author	Zhang, Huizi
URN	etd-08192006-115350
Title	Classification Analysis for Environmental Monitoring: Combining Information across Multiple Studies
Degree	PhD
Department	Statistics
Advisory Committee	Advisor Name Title Eric P. Smith Committee Chair Edward Boone Committee Member Keying Ye Committee Member Samantha Bates Prins Committee Member
Keywords	Environmental studies Clustering Hierarchical Model Classification
Date of Defense	2006-08-14
Availability	restricted
Abstract 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.
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