%0 Journal Article
%T An Improved Robust Regression Model for Response Surface Methodology
%A Edionwe
%A Efosa
%A Ekhosuehi
%A N.
%A Mbegbu
%A J. I.
%A Obiora-Ilouno
%A H. O.
%J -
%D 2018
%R 10.17535/crorr.2018.0025
%X Sa£żetak 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
%K desirability function
%K genetic algorithm
%K local linear regression
%K multiple response optimization problem
%K semi-parametric regression models
%U https://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=310575