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A Bayesian Model-Averaging Approach for Multiple-Response Optimization
16 references·1 citing·37 second hop
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References

  • 1995Journal of the American Statistical Association5,337 cited
  • 1992Optimum Experimental Designs815 cited
  • 1989Introduction to Quality Engineering: Designing Quality Into Products and Processes629 cited
  • 2001Experiments: Planning, Analysis, and Parameter Design Optimization536 cited
  • 1993STRATEGIES FOR ROBUST MULTIRESPONSE QUALITY ENGINEERING372 cited
  • 1998A Compromise Approach to Multiresponse Optimization244 cited
  • 1994A Simple Bayesian Modification of D -Optimal Designs to Reduce Dependence on an Assumed Model174 cited
  • 1993Finding the Active Factors in Fractionated Screening Experiments171 cited
  • 2001Analyzing Experiments with Correlated Multiple Responses155 cited
  • 1995Optimal Design via Curve Fitting of Monte Carlo Experiments140 cited
  • 2004A Posterior Predictive Approach to Multiple Response Surface Optimization139 cited
  • 1996Follow-Up Designs to Resolve Confounding in Multifactor Experiments106 cited
  • 2001Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms101 cited
  • 2004A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables83 cited
  • 2006Bayesian Process Monitoring, Control and Optimization

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65 cited
  • 2005Model-Robust Process Optimization Using Bayesian Model Averaging45 cited
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    • 2016A new loss function for multi-response optimization with model parameter uncertainty and implementation errors37 cited