Aliasing in Random Field Model for Qualitative Factors with Symmetric Levels

• Activity Number: 507
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Ming-Chung Chang*+ and Shao-Wei Cheng and Ching-Shui Cheng
• Companies: and National Tsing Hua University and Academia Sinica
• Keywords: Fractional factorials ; Bayesian design ; computer experiments ; kriging model

Abstract

Factorial designs are often used in scientific investigations when the interest of experimenters lies in the study of factorial effects. Fractional factorial designs are commonly used in practice for the economic reasons. Effect aliasing is a consequence of using fractional factorial designs. Under the fixed effects approach, effect aliasing has been extensively studied and well understood. An alternative modeling framework for experimental data is the Gaussian random field model, which is commonly adopted in the literature of Bayesian designs and computer experiments. For the Gaussian random field model, the issue of effect aliasing under fractional factorial designs has not received sufficient attention in the literature. Part of the reason for this lack of attention is that the Gaussian random field model is usually not characterized by a linear model structure that is inherent in the fixed effects model. In the article, we introduce the concept of factorial effects and establish a kind of linear model structure for the Gaussian random field model, and discuss effect aliasing under the structure for p-level fractional factorial designs, where p is a prime number.