Evaluation of the predictive performance of the r-k and r-d class estimators

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.

KEYWORDS:

• Biased estimation
• r-k Class estimator
• r-d Class estimator
• multicollinearity
• Prediction Mean Square Error.

MATHEMATICS SUBJECT CLASSIFICATIONS:

• 62J05
• 62J07