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 Title: Regression modeling: Latent structure, theories and algorithms Author(s): Xie, Minge Doctoral Committee Chair(s): Simpson, Douglas G. Department / Program: Statistics Discipline: Statistics Degree Granting Institution: University of Illinois at Urbana-Champaign Degree: Ph.D. Genre: Dissertation Subject(s): Biology, Biostatistics Statistics Environmental Sciences Abstract: 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. Issue Date: 1996 Type: Text Language: English URI: http://hdl.handle.net/2142/20609 Rights Information: Copyright 1996 Xie, Minge Date Available in IDEALS: 2011-05-07 Identifier in Online Catalog: AAI9625216 OCLC Identifier: (UMI)AAI9625216
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