Prediction of values of variables in linear measurement error model

This article presents the framework of a linear measurement error model for analysing the data in environmental studies where observations on variables are subjected to measurement errors. The problem of predicting the average and actual values of such variables separately as well as simultaneously are discussed. The methods of ordinary least squares, joint least squares and generalized least squares are employed to construct the predictors. Efficiency properties of these predictors are derived and their comparative study is made. These predictors are exposed to a data set and their performance properties are analysed.