An Improved Robust Regression Model for Response Surface Methodology
| Autor: | J. I. Mbegbu, Efosa Edionwe, H. O. Obiora-Ilouno, N. Ekhosuehi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Economics and Econometrics Mathematical optimization Computer science Applied Mathematics lcsh:T57-57.97 Local regression Regression analysis Management Science and Operations Research Robust regression desirability function genetic algorithm local linear regression multiple response optimization problem semi-parametric regression models Ordinary least squares lcsh:Applied mathematics. Quantitative methods Data analysis Process optimization Sensitivity (control systems) Response surface methodology Statistics Probability and Uncertainty |
| Zdroj: | Croatian Operational Research Review, Vol 9, Iss 2, Pp 317-330 (2018) Croatian Operational Research Review Volume 9 Issue 2 |
| ISSN: | 1848-9931 1848-0225 |
| Popis: | In production, manufacturing and several other allied industries, appropriate tool is applied in the analysis of data in order to enhance the opportunity for product and process optimization. A statistical tool that has successfully been used to achieve this goal is Response Surface Methodology (RSM). A recent trend in the modeling phase of RSM involves the use of semi-parametric regression models which are hybrids of the Ordinary Least Squares (OLS) and the Local Linear Regression (LLR) models. In this paper, we propose a modification in the current structure of the semi-parametric Model Robust Regression 2 (MRR2) with a view to improving its sensitivity to local trends and patterns in data. The proposed model is applied to two multiple response optimization problems from the literature. The results of goodness-of-fits and optimal solutions confirm that the proposed model performs better than the MRR2. |
| Databáze: | OpenAIRE |
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