Robust Parameter Design of Functional Responses Based on Bayesian SUR Models

摘要：

　　As for the robust parameter design of functional responses, a Bayesian Seemingly Unrelated Regression (SUR) model is proposed to take into account the model uncertainty and response variability in this paper.First of all, the SUR model is used to build the functional relationship between the output responses and the input factors at different time points.Also,Bayesian analysis of the SUR model is performed to consider the influence of the model parameter uncertainty on the research results.Secondly, the process means and variances of the functional responses at different time points are estimated by the posterior samples of the simulated responses.Moreover, an integrated performance index (i.e.mean square error) is establish by using the above process means and variances.Then, the optimal parameter settings may be found by minimizing the MSE performance index.Finally, the advantages of the proposed method are illustrated by an example from the literature.

关键词： Robust parameter design functional responses Bayesian analysis Seemingly Unrelated Regression

作者: Xiaolong Zhou Jianjun Wang Jun Ma Yiliu Tu

作者单位: School of Economics and Management, Nanjing University of Science and Technology,Nanjing, 210094, Ch Department of Mechanical and Manufacturing Engineering, University of Calgary,University Drive 2500,

会议类型: 国际会议

会议名称: The Eighteenth Wuhan International Conference on E-Business(第18届武汉电子商务国际会议)

会议地点: 武汉

会议语种:英文

页码: 641-648

在线出版日期: 2019-05-24（万方平台首次上网日期，不代表论文的发表时间）