A Repeated Significance Test With Applications To Sequential Detection In Sensor Networks

In this paper we introduce a
randomly
truncated sequential hypothesis test. Using the framework of a repeated significance test (RST), we study a sequential test with truncation time based on a random stopping time. Using the functional central limit theorem (FCLT) for a sequence of statistics, we derive a general result that can be employed in developing a repeated significance test with random sample size. We present effective methods for evaluating accurate approximations for the probability of type I error and the power function. Numerical results are presented to evaluate the accuracy of these approximations. We apply the proposed test to a decentralized sequential detection problem in sensor networks (SNs) with communication constraints. Finally, a sequential detection problem with measurements at random times is investigated.