BEST LINEAR UNBIASED ESTIMATORS FOR THE MULTIPLE LINEAR REGRESSION MODEL USING RANKED SET SAMPLING WITH A CONCOMITANT VARIABLE

Ranked set sampling is a more efficient sampling technique than simple random sampling for estimating the population mean when the measurement of the sampling units according to the variable of interest is expensive or difficult, but ranking them is relatively cheap and easy. In this study, the best linear unbiased estimators in the class of linear combinations of the ranked set sample values are obtained for multiple linear regression models with replicated observations. During the sample selection procedure of ranked set sampling, it is assumed that the ranking is done according to a concomitant variable. The estimators obtained from the ranked set sampling and simple random sampling with the same sample size are compared with respect to the relative efficiency.