A semi-parametric approach to robust parameter design

• Published in: Journal of statistical planning and inference Vol. 138; no. 1; pp. 114 - 131
• Main Authors: Pickle, Stephanie M., Robinson, Timothy J., Birch, Jeffrey B., Anderson-Cook, Christine M.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Lausanne Elsevier B.V 2008; New York,NY Elsevier Science; Amsterdam
• Subjects: Combinatorics; Combinatorics. Ordered structures; Designs and configurations; Exact sciences and technology; Experimental design; General topics; Genetic algorithm; Linear inference, regression; Mathematics; Model robust regression; Nonparametric regression; Probability and statistics; Response surface; Sciences and techniques of general use; Statistics; Response surface; Genetic algorithm; Nonparametric regression; Model robust regression; Parameter estimation; Covariance analysis; Variance estimation; Bias; Non parametric estimation; Statistical decision; Parametric estimation; Variance analysis; Mean estimation; Variance; Statistical method; Statistical regression; Simulation; Experimental design; Quality control; Curvature; Biased estimation
• ISSN: 0378-3758; 1873-1171
• DOI: 10.1016/j.jspi.2007.05.018

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.