title: Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.
abstract: In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.
authors.affiliation: Department of Statistics, Faculty of Science and Letters, Mimar Sinan Fine Arts University, Sisli, 34380 Istanbul, Turkey.
authors.forename: Ali
authors.initials: A
authors.lastname: Erkoc
authors.affiliation: Department of Mathematics, Faculty of Science, Istanbul University, Beyazit, 34134 Istanbul, Turkey.
authors.forename: Esra
authors.initials: E
authors.lastname: Emiroglu
authors.affiliation: Department of Mathematics, Faculty of Science, Istanbul University, Beyazit, 34134 Istanbul, Turkey.
authors.forename: Kadri Ulas
authors.identifier: 0000-0002-8668-2879
authors.initials: KU
authors.lastname: Akay
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical