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https://doi.org/10.17535/crorr.2018.0025

An Improved Robust Regression Model for Response Surface Methodology

Efosa Edionwe ; Department of Mathematical Sciences, Edwin Clark University, Delta State, Nigeria
J. I. Mbegbu ; Department of Statistics, University of Benin, Benin, Nigeria
N. Ekhosuehi ; Department of Statistics, University of Benin, Benin, Nigeria
H. O. Obiora-Ilouno ; Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria

Puni tekst: engleski, pdf (3 MB) str. 317-330 preuzimanja: 241* citiraj
APA 6th Edition
Edionwe, E., Mbegbu, J.I., Ekhosuehi, N. i Obiora-Ilouno, H.O. (2018). An Improved Robust Regression Model for Response Surface Methodology. Croatian Operational Research Review, 9 (2), 317-330. https://doi.org/10.17535/crorr.2018.0025
MLA 8th Edition
Edionwe, Efosa, et al. "An Improved Robust Regression Model for Response Surface Methodology." Croatian Operational Research Review, vol. 9, br. 2, 2018, str. 317-330. https://doi.org/10.17535/crorr.2018.0025. Citirano 05.08.2021.
Chicago 17th Edition
Edionwe, Efosa, J. I. Mbegbu, N. Ekhosuehi i H. O. Obiora-Ilouno. "An Improved Robust Regression Model for Response Surface Methodology." Croatian Operational Research Review 9, br. 2 (2018): 317-330. https://doi.org/10.17535/crorr.2018.0025
Harvard
Edionwe, E., et al. (2018). 'An Improved Robust Regression Model for Response Surface Methodology', Croatian Operational Research Review, 9(2), str. 317-330. https://doi.org/10.17535/crorr.2018.0025
Vancouver
Edionwe E, Mbegbu JI, Ekhosuehi N, Obiora-Ilouno HO. An Improved Robust Regression Model for Response Surface Methodology. Croatian Operational Research Review [Internet]. 2018 [pristupljeno 05.08.2021.];9(2):317-330. https://doi.org/10.17535/crorr.2018.0025
IEEE
E. Edionwe, J.I. Mbegbu, N. Ekhosuehi i H.O. Obiora-Ilouno, "An Improved Robust Regression Model for Response Surface Methodology", Croatian Operational Research Review, vol.9, br. 2, str. 317-330, 2018. [Online]. https://doi.org/10.17535/crorr.2018.0025

Sažetak
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.

Ključne riječi
desirability function; genetic algorithm; local linear regression; multiple response optimization problem; semi-parametric regression models

Hrčak ID: 212397

URI
https://hrcak.srce.hr/212397

Posjeta: 462 *