Evaluation of the predictive performance of the r-k and r-d class estimators. Issue 8 (18th April 2017)

• Record Type: Journal Article
• Authors: Dawoud, Issam; Kaçıranlar, Selahattin
• Is Part Of: Communications in statistics. Volume 46:Issue 8(2017)
• Page Start: 4031
• Page End: 4050
• Publication Date: 2017-04-18
• Subjects: Biased estimation -- r-k Class estimator -- r-d Class estimator -- multicollinearity -- Prediction Mean Square Error.; 62J05 -- 62J07; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2015.1076482
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: Multiple linear regression models are frequently used in predicting unknown values of the response variable y . In this case, a regression model's ability to produce an adequate prediction equation is of prime importance. This paper discusses the predictive performance of the r-k and r-d class estimators compared to ordinary least squares (OLS), principal components, ridge regression and Liu estimators and between each other. The theoretical results are illustrated using Portland cement data and a region is established where the r-k and the r-d class estimators are uniformly superior to the other mentioned estimators.