Degenerate-generalized likelihood ratio test for one-sided composite hypotheses

We propose the degenerate-generalized likelihood ratio test (DGLRT) for one-sided composite hypotheses in cases of independent and dependent observations. The theoretical results show that the DGLRT has controlled error probabilities and stops sampling with probability 1 under some regularity conditions. Moreover, its stopping boundaries are constants and can be easily determined using the provided searching algorithm. According to the simulation studies, the DGLRT has less overall expected sample sizes and less relative mean index (RMI) values in comparison with the sequential probability ratio test (SPRT) and double sequential probability ratio test (2-SPRT). To illustrate the application of it, a real manufacturing data are analyzed.