A Comparison of Location Effect Identification Methods for Unreplicated Fractional Factorials in the Presence of Dispersion Effects
Unreplicated fractional factorial designs are usually used to identify location effects and dispersion effects in screening experiments. Various methods for identifying active location effects have been proposed during last three decades. All of these methods depend on the assumption of no dispersion effects; meanwhile most of dispersion-identification methods rely on first identifying the correct location effect model.
The presence of dispersion effects induces correlation among location effect estimates. If location-effect identification methods are sensitive to this correlation, then finding the correct location model may be more difficult in the presence of dispersion effects.
The primary aim of this project is to compare the robustness of different location-identification methods - Box and Meyer (1986), Lenth (1989), Berk and Picard (1991), and Loughin and Noble (1997) - under the heteroscedastic model via simulation studies. Confounding of location and dispersion effects has also been investigated here. The first three methods perform ffne with respect to error rates and power, but the last one loses control of the individual error rate when moderate-to-large dispersion effects are present.
This type of interdisciplinary work is a hallmark of our program in Applied Statistics at Simon Fraser University. For more information, please contact Yan Zhang (firstname.lastname@example.org) or her supervisor Tom Loughin (email@example.com), Department of Statistics and Actuarial Science, Simon Fraser University.
Key Words: Heteroscedastic model; Lenth (1989); Berk and Picard (1991); Box and Meyer (1986); Loughin and Noble (1997); Correlation; IER; EER; Power; Simulation.