摘要：Modeling real data sets, even when we have some potential (as)symmetric models forthe underlying data distribution, is always a very di.cult task due to some uncon-trollable perturbation factors. The analysis of di.erent data sets from diverse areasof application, and in particular from statistical process control (SPC), leads us tonotice that they usually exhibit moderate to strong asymmetry as well as light toheavy tails, which leads us to conclude that in most of the cases, fitting a normaldistribution to the data is not the best option, despite of the simplicity and popu-larity of the Gaussian distribution. In this paper we consider a class of skew-normalmodels that include the normal distribution as a particular member. Some propertiesof the distributions belonging to this class are enhanced in order to motivate their usein applications. To monitor industrial processes some control charts for skew-normaland bivariate normal processes are developed, and their p erformance analyzed. Anapplication with a real data set from a cork stopper's process production is presented
关键词：bootstrap control charts; false alarm rate; heavy-tails; Monte Carlo simulations;probability limits; run-length; shewhart control charts; skewness; skew-normal dis-;tribution; statistical process control
