Ridge Regression Estimation for Survey Samples

Abstract

This paper describes procedure for constructing a vector of regression weights. Under the regression superpopulation model, the ridge regression estimator that has minimum model mean squared error is derived. Through a simulation study, we compare the ridge regression weights, regression weights, quadratic programming weights, and raking ratio weights. The ridge regression procedure with weights bounded by zero performed very well.

Keywords:

• Design consistency
• Model MSE
• Regression superpopulation model
• Ridge regression

Mathematics Subject Classification:

• Primary 62D05
• Secondary 62J07