Please use this identifier to cite or link to this item:
Title: A Bayesian model-averaging approach for multiple-response optimization
Authors: Ng, S.H. 
Keywords: Decision theoretic approach
Follow-Up sampling
Optimal design
Preposterior analysis
Quality loss functions
Issue Date: Jan-2010
Citation: Ng, S.H. (2010-01). A Bayesian model-averaging approach for multiple-response optimization. Journal of Quality Technology 42 (1) : 52-68. ScholarBank@NUS Repository.
Abstract: The characteristics that define the quality and reliability of many products and processes are often multidimensional. Many of the current multiple-response optimization approaches assume a single-response model to optimize such processes and do not consider the correlations among the response data, the uncertainty in the response models, and the uncertainty in the parameter estimates of the models. Failure to account for these uncertainties can result in misleading quality estimates and therefore poor process design. In this paper, we consider a Bayesian decision theoretic approach to the modeling and optimization of multiple-response systems. This approach naturally accounts for the correlations among the responses, the variability of the predictions, and the uncertainty of the model parameters. We further propose a Bayesian model averaging approach to account for response-model uncertainty. This approach is general and enables the consideration of many types of quality criteria and characteristics. In addition, we also consider the important follow-up question of how to allocate further resources for additional experimentation to achieve or improve on the desired quality level.
Source Title: Journal of Quality Technology
ISSN: 00224065
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Jan 11, 2021

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.