Robust statistical process control in Block-RDT framework

Random distortion testing (RDT) introduced in [1] is aimed at detecting any significantly big distortion of a signal with respect to a model of this signal, in presence of noise and without prior knowlegde on the distortion distribution. The RDT formulation makes it possible to state the standard change-in-mean detection problem differently. It leads to the Block-RDT approach that requires no iid assumption and no prior knowledge on the distributions of the observations before and after change. The optimal tests derived in the Block-RDT approach are alternative to Shewhart charts and outperform the latter by accounting for possible model mismatches that cause likelihood theory to fail. Experimental results dedicated to the detection of steps in a random process illustrate our intention.