A Dual-Response Approach to Robust Parameter Design for a Generalized Linear Model

Abstract

Robust Parameter Design (RPD) has been used extensively in industrial experiments since its introduction by Genichi Taguchi. RPD has been studied and applied, in most cases, assuming a linear model under standard assumptions. More recently, RPD has been considered in a generalized linear model (GLM) setting. In this paper, we apply a general dual-response approach when using RPD in the case of a GLM. We motivate the need for exploring both the process mean and process variance by discussing situations when a compromise between the two is necessary. Several examples are provided in order to further motivate the need for applying a dual-response approach when applying RPD in the case of a GLM.

• Dual-Response Approach
• Dispersion Effects
• Generalized Linear Models
• Noise Variables
• Response Surface Methodology
• Robust Parameter Design

Additional information

Notes on contributors

William R. Myers

Dr. Myers is Head of the Statistical Quality Control Section within the Biometrics and Statistical Sciences Department. He is a member of ASQ. His e-mail address is [email protected].

William A. Brenneman

Dr. Brenneman is a Senior Statistician in the Statistical Quality Control Section within the Biometrics and Statistical Sciences Department. He is a member of ASQ. His e-mail address is [email protected].

Raymond H. Myers

Dr. Myers is Professor Emeritus of Statistics. He is a member of ASQ. His e-mail address is [email protected]