Gini’s Mean Difference-Based Minimum Risk Point Estimator of Mean

This chapter considers a loss function which considers both the sampling cost and error in estimation of the population mean. The error in estimation of a population parameter increases when the sample size decreases and the error in estimation decreases when the sample size increases. The chapter focuses on a Gini’s mean difference-based, purely sequential procedure for estimating the population mean. Then the estimator of the population mean, based on the estimated final sample size, is called the minimum risk point estimator if the corresponding risk in estimating the population mean is asymptotically close to the minimized risk. The accuracy of an estimator of a parameter increases if we increase the sample size, but this in turn increases the overall sampling cost. The chapter discusses a Gini’s mean difference-based purely sequential procedure which minimizes both the estimation error and the overall sampling cost.