Estimating the variance of a normal population by utilizing the information in a sample from a second related normal population

Abstract

A class of estimators of the variance σ1 2 of a normal population is introduced, by utilization the information in a sample from a second normal population with different mean and variance σ2 2, under the restriction that σ1 2 ≤ σ2 2. Simulation results indicate that some members of this class are more efficient than the usual minimum variance unbiased estimator (MVUE) of σ1 2, Stein estimator and Mehta and Gurland estimator. The case of known and unknown means are considered.