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Simpson, Douglas G. Xie, Minge 2011-05-07T12:44:11Z 2011-05-07T12:44:11Z 10000-01-01 1996 The topics of this thesis stem from two EPA/NISS (Environmental Protection Agency/National Institute of Statistical Sciences) projects, which require the use of available data to make risk assessment, estimate uncertainty and suggest future studies. Based on the heterogeneous and batch correlated nature of the data, the thesis invents some new regression modeling methods, provides theoretical background for these newly developed and some other existed ad hoc modeling techniques, and develops associated algorithms. The modeling techniques include scaled link in the class of generalized linear model, newly developed aspects of conditional and marginal modeling techniques, and latent modeling of nonzero control (baseline) regression model. We have Monte-Carlo-Newton-Raphson Algorithm, Gibbs Sampler, EM algorithm and algorithm to evaluate weighted sum $\chi\sp2$ quantile. The associated theories are provided. In scaled link model, some sensitivity studies are made. Made available in DSpace on 2011-05-07T12:44:11Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9625216.pdf: 3896820 bytes, checksum: fa5cd04c69645ee5dc642b4b20b4244b (MD5) Previous issue date: 1996 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:45:03Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:19:54-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9625216 (UMI)AAI9625216 http://hdl.handle.net/2142/20609 eng Copyright 1996 Xie, Minge Biology, Biostatistics Statistics Environmental Sciences Regression modeling: Latent structure, theories and algorithms text Statistics Statistics University of Illinois at Urbana-Champaign Dissertation Ph.D.