Optimal designs are computer-generated experimental designs that provide an experimenter with an ‘optimal’ set of experimental trials. Historically, optimal experimental design has been limited to optimization with regards to a single criterion for a single response variable. Recent research by Burke et al. (2017) made it possible to create a dual response optimal designs for cases involving experiments with one continuous response and one binary response. The algorithm in Burke et al. (2017) provides a series of weighted optimal designs across a range of weights between the continuous and binary response cases. This thesis extends the work by Burke et al. (2017) in three ways. First, a new optimality criterion is developed in order to provide more stable algorithm results. Second, a method for selecting the weighted design that provides the best results for the continuous and binary cases is developed. Finally, a sensitivity analysis on the prior information required to generate the optimal designs in performed.

Library of Congress Subject Headings

Experimental design--Data processing; Optimal designs (Statistics); Bayesian statistical decision theory

Publication Date


Document Type


Student Type


Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)


Rachel Silvestrini

Advisor/Committee Member

Katie McConky


RIT – Main Campus

Plan Codes