{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/87421"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/87421","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Censored Regression Models With Applications to Infrastructure Degradation Studies","abstract":"Motivated by consulting in infrastructure studies, we consider the estimation and inference for regression models where the response variable is bounded or censored. In these conditions, least squares methods are not appropriate, although they are widely used. This dissertation develops a generalization of the Tobit censored regression model using Student's t distribution instead of the normal. Thus additional flexibility is achieved varying the degrees of freedom. The variance function can be estimated by using additional regression steps. Nonparametric methods are extended to apply to bounded or censored data. For correlated measurements, random effect models and generalized estimating equation methods for censored data are developed. A general argument about the theoretical bias for the GEE method, both in the censored and uncensored case, provides a criteria for deciding when marginal analysis is appropriate. We apply the methods to a cross-sectional study of factors influencing roof condition as a function of age and a mixed cross-sectional and longitudinal study on road conditions.","abstract_html":"Motivated by consulting in infrastructure studies, we consider the estimation and inference for regression models where the response variable is bounded or censored. In these conditions, least squares methods are not appropriate, although they are widely used. This dissertation develops a generalization of the Tobit censored regression model using Student's t distribution instead of the normal. Thus additional flexibility is achieved varying the degrees of freedom. The variance function can be estimated by using additional regression steps. Nonparametric methods are extended to apply to bounded or censored data. For correlated measurements, random effect models and generalized estimating equation methods for censored data are developed. A general argument about the theoretical bias for the GEE method, both in the censored and uncensored case, provides a criteria for deciding when marginal analysis is appropriate. 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