Censored Regression Models With Applications to Infrastructure Degradation Studies

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Galfalvy, Hanga Csilla

Contributors dc:contributor

• Simpson, Douglas G.

• Statistics

Language dc:language

eng

Identifier

(MiAaPQ)AAI9990000

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/87421