Estimating Multiple Step Shifts in a Gaussian Process Mean with an   Application to Phase I Control Chart Analysis
  In preliminary analysis of control charts, one may encounter multiple shifts and/or outliers especially with a large number of observations. The following paper addresses this problem. A statistical model for detecting and estimating multiple change points in a finite batch of retrospective (phase I)data is proposed based on likelihood ratio test. We consider a univariate normal distribution with multiple step shifts occurred in predefined locations of process mean. A numerical example is performed to illustrate the efficiency of our method. Finally, performance comparisons, based on accuracy measures and precision measures, are explored through simulation studies. 
Issac Shams, Saeede Ajorlou, and Kai Yang
stat.AP