2026-09-20T11:22:11Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/14592 2023-07-10 col_2142_5131 col_2142_14737 com_2142_5130 com_2142_690 com_2142_397

Gertner, George Z. Gertner, George Z. Douglas, Jeffrey A. Juvik, John A. Onstad, David W. Fan, Cha-Chi 2010-01-06T16:13:32Z 2010-01-06T16:13:32Z 2010-01-06T16:13:32Z Correlated parameters are often expected when modeling a natural system. However, correlation among the variables often blurs the model uncertainty and makes it difficult to determine parameter sensitivity. In simple systems, model structure and uncertainty can be explained directly; however, uncertainty and parameter relationships in model structures of complex systems, such as process models and spatial models, are not usually straightforward. A common data examination method used to identify the variable relationships is a variable correlation matrix or covariance matrix. A variable covariance matrix is a composite of variable causality, correlation, and other interactions. In other words, the variable covariance matrix contains information about the relationship of variables in a system. Therefore, the variable covariance matrix in a model is expected to match the natural system being modeled. However, the true covariance matrix of the natural system is not always known. A covariance structure analysis, based on structural equation modeling, is a potential way to search for explanation of the covariance related to the modeled system. The main subject in this study is to investigate the use of covariance structure analysis of covariance matrices among variables (both independent and dependent) in a system to explain the parameter causal relationships and sensitivities. Three case studies, including two simple nonlinear regression models, a forest process model, and a spatial dynamic host-parasitoid interaction model, will be used to illustrate this idea. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2009-11-09T19:49:04Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Fan_ChaChi.pdf: 4316364 bytes, checksum: f18fb733e1d05612adaa8592703cd0c0 (MD5) 4_thesis_all_modified_101809.doc: 13554688 bytes, checksum: ad15aae4ce460c1f411dbdde29593241 (MD5) Made available in DSpace on 2010-01-06T16:13:32Z (GMT). No. of bitstreams: 4 license.txt: 4054 bytes, checksum: 46ef7a743b4d725194f147c21b9e53fa (MD5) 4_thesis_all_modified_101809.doc: 13554688 bytes, checksum: ad15aae4ce460c1f411dbdde29593241 (MD5) Fan_ChaChi.pdf: 4316364 bytes, checksum: f18fb733e1d05612adaa8592703cd0c0 (MD5) 1_Fan_ChaChi.pdf: 4313887 bytes, checksum: 88916e0af8338bf5befccb1e6a66319d (MD5) http://hdl.handle.net/2142/14592 en Copyright 2009 Cha-Chi Fan uncertainty analysis sensitivity analysis uncertainty partitioning correlation covariance structure analysis covariance structural model pipe model forest process model host and parasitoid interaction spatial dynamic model Examination of model uncertainty and parameter sensitivity in correlated systems using covariance structure analysis 2009-12 Natural Res & Env Sci 1875 Natural Res & Env Sciences 0190 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Nat Res & Envrn Sci -UIUC 10KS0190PHD