Empirical Bayes Prediction in a Sequential Sampling Plan Based on Loss Functions

The application of empirical Bayes for lot inspection in sequential sampling plans is usually conducted to estimate the proportion of defective items in the lot rather than for hypothesis testing of the variables' process mean. In this paper, we propose the use of empirical Bayes in a sequential sampling plan variables' process mean testing under a squared error loss function and precautionary loss function, for which the prediction is performed to estimate a sequence of the mean when the data are normally distributed in the case of a known mean and unknown variance. The proposed plans are compared with the sequential sampling plan. The proposed techniques yielded smaller average sample number (ASN) and provided higher probability of acceptance (P-a) than the sequential sampling plan.