Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.

Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Using nonlinear non-monotonic hormetic models and designs for detecting and estimating hormesis

Chunhao Tu, University of Nebraska - Lincoln

Abstract

Nonlinear models with switching functions are flexible and powerful tools for the analysis of hormetic data in many areas such as biology, public health, and toxicology. The purpose of this dissertation is to study ways of detecting and estimating hormesis precisely by using nonlinear models and designs. In this dissertation, we propose a new non-monotonic weighting function that can be used with any type of switching function to form a nonlinear non-monotonic hormetic model, which can be used to detect hormesis and estimate the location and the size of the maximum hormetic effect. Based on the nonlinear non-monotonic hormetic model, a sub-partial nonlinear regression method (SPNRM) is proposed to maximize the D-optimality criteria of a nonlinear hormetic design. Following that, we create a hormesisNMF module based on the R platform to provide users a user-friendly interface for analyzing hormetic data. Finally, we apply the logistic model with the non-monotonic weighting function to Escherichia coli (E. coli) data to examine the differences between various E. coli groups. We find that the new non-monotonic weighting function can be used in various scenarios to obtain the least biased parameter estimates.^

Subject Area

Biology, Biostatistics|Statistics

Recommended Citation

Tu, Chunhao, "Using nonlinear non-monotonic hormetic models and designs for detecting and estimating hormesis" (2009). ETD collection for University of Nebraska - Lincoln. AAI3360087.
http://digitalcommons.unl.edu/dissertations/AAI3360087

Share

COinS