The Efficiencies of Maximum Likelihood and Minimum Variance Unbiased Estimators of Fraction Defective in the Normal Case

This paper compares two point estimators of fraction defective of a normal distribution when both population parameters are unknown; the rflinimum variance unbiased estimator, P(z), and the maximum likelihood estimator, F(x). Using minimum mean squared error as a criterion, i t is shown that the choice of estimator depends upon the true value of F(x), and the sample size. In the domain ,0005 5 F(x) 5 .50, the maximum likelihood estimator is generally superior even for small sample sizes, except for F(x) less than about 0.01, or greater than 0.25. Furthermore, the bias in the m.1.e. is slight over much of the domain where this estimator has smaller mean squared error. As a practical solution to the estimation problem. it is suggested that the m.v.u.e. be calculated, and if this estimate is between 0.01 and 0.25, it should be replaced with the m.1.e. This combined estimator is shown to be nearly as efficient as the better of the m.v.u.e. and m.1.e. throughout the domain of F(x).