Bounded-Influence Regression Estimation for Mixture Experiments

Home Page
About
Submit A Journal
Submit A Conference
Submit Paper/Book
- Submit a Preprint
- Submit a Book
Contact

Mersin Üniversitesi Eğitim Fakültesi Dergisi
Volume:14 Issue:3
Bounded-Influence Regression Estimation for Mixture Experiments

Bounded-Influence Regression Estimation for Mixture Experiments

Pages : 1020-1037

View : 18 | Download : 13

Publication Date : 2018-12-25

Article Type : Research Paper

Abstract :Ordinary Least Squares insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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

ORIGINAL ARTICLE URL

VIEW PAPER (PDF)

* There may have been changes in the journal, article,conference, book, preprint etc. informations. Therefore, it would be appropriate to follow the information on the official page of the source. The information here is shared for informational purposes. IAD is not responsible for incorrect or missing information.