Prediction of Response Values in Linear Regression Models from Replicated Experiments

This paper considers the problem of prediction in a linear
regression model when data sets are available from replicated
experiments. Pooling the data sets for the estimation of regression
parameters, we present three predictors - one arising from the
least squares method and two stemming from Stein-rule method.
Efficiency properties of these predictors are discussed when they
are used to predict actual and average values of response variable
within/out-side the sample. Key words: least squares estimator,
prediction, Stein-type estimator