Bounded-Influence Regression Estimation for Mixture Experiments

Year 2018, Volume: 14 Issue: 3, 1020 - 1037, 25.12.2018

Orkun Coşkuntuncel

https://doi.org/10.17860/mersinefd.443584

Abstract

Ordinary
Least Squares (OLS) estimator is widely used technique for estimating the
regression coefficient in mixture experiments. But this estimator is very
sensitive to outliers and/or multicollinearity problems. The aim of this paper
is to propose estimators for the regression parameters of a mixture model that
can combat with the above problems. For this purpose, Generalized M (GM)
estimation, which is more resistant to outliers in the y and / or x directions
and regression estimators such as ridge and Liu, which is effective against the
multicollinearity, were used together. The Mean Square Error (MSE) properties
of proposed estimator has been examined and shown to be smaller than biased and
GM estimates. Also performance of the combined estimator is illustrated by
examples.

Keywords

Regression, Ridge regression, Liu estimator, Robustness, GM estimator

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