A0719
Title: Design construction and model selection for small mixture-process variable experiments with high-dimensional model terms
Authors: Kashinath Chatterjee - Augusta University (United States)
Chang-Yun Lin - National Chung Hsing University (Taiwan) [presenting]
Abstract: The design construction and model selection for mixture-process variable experiments where the number of variables is large is considered. For such experiments, the generalized least squares estimates cannot be obtained, and hence it will be difficult to identify the important model terms. To overcome these problems, the generalized Bayesian-D criterion is employed to choose the optimal design and the Bayesian analysis method is applied to select the best model. Two algorithms are developed to implement the proposed methods. A fish-patty experiment demonstrates how the Bayesian approach can be applied to a real experiment. Simulation studies show that the proposed method has a high power to identify important terms and well control the type I error.
