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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 3.072 Kb

str. 317-330

preuzimanja: 329



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

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