Scholarly
    Communications Project


Document Type:Dissertation
Name:Jennifer Wade Huffman
Email address:jwhf@lubrizol.com
URN:1998/01122
Title:Optimal Experimental Design for Poisson Impaired Reproduction Studies
Degree:Doctor of Philosophy
Department:Statistics
Committee Chair: Raymond H. Myers
Chair's email:rmyers@vt.edu
Committee Members:Clint W. Coakley
Robert V. Foutz
Marvin Lentner
Keying Ye
Keywords:response surface methodology, poisson exponential model, equivalence theory, Bayesian optimal designs
Date of defense:July 23, 1998
Availability:Release the entire work for Virginia Tech access only.
After one year release worldwide only with written permission of the student and the advisory committee chair.

Abstract:

Impaired reproduction studies with Poisson responses are among a growing class of toxicity studies in the biological and medical realm. In recent years, little effort has been focused on the development of efficient experimental designs for impaired reproduction studies. This research concentrates on two areas: 1) the use of Bayesian techniques to make single regressor designs robust to parameter misspecification and 2) the extension of design optimality methods to the k-regressor model. The standard Poisson model with log link is used. Bayesian designs with priors on the parameters are explored using both the D and F-optimality criteria for the single regressor Poisson exponential model. Since these designs are found via numeric optimization techniques, Bayesian equivalence theory functions are derived to verify the optimality of these designs. Efficient Bayesian designs which provide for lack-of-fit testing are discussed. Characterizations of D, Ds, and interaction optimal designs which are factorial in nature are demonstrated for models involving interaction through k factors. The optimality of these designs is verified using equivalence theory. In addition, augmentations of these designs that result in desirable lack of fit properties are discussed. Also, a structure for fractional factorials is given in which specific points are added one at a time to the main effect design in order to gain estimability of the desired interactions. Robustness properties are addressed as well. Finally, this entire line of research is extended to industrial exponential models where different regressors work to increase and/or decrease a count data response produced by a process.

List of Attached Files

appa.pdf appb.pdf appc.pdf
appd.pdf appe.pdf appf.pdf
appg.pdf bib.pdf ch1.pdf
ch10.pdf ch11.pdf ch12.pdf
ch2.pdf ch3.pdf ch4.pdf
ch5.pdf ch6.pdf ch7.pdf
ch8.pdf ch9.pdf etd.pdf
vita.pdf

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