In recent years, some authors have incorporated the penalized likelihood estimation into designing multivariate control charts under the premise that in practice typically only a small set of variables actually contributes to changes in the process. The advantage of the penalized likelihood estimation is that it produces sparse and more focused estimates of the unknown population parameters whi...

nowadays statistical control process plays an important role in quality control of products. so wide variety of methods are utilized to do so. but since the most percentage of available information in the discrete control charts are verbal terms, fuzzy and vague ,in most cases it is difficult for us to refine them into the quantitative data. thus in this article we are to change these verbal da...

Abstract: Today's society is characterized by consumers becoming more educated and demanding for products and services they use. Wherever, organizations are structured to respond the explicit or implicit needs of consumers, which lead to increased competitiveness of organizations. This competitiveness is reflected in the ongoing quest to provide high quality products and excellence process deve...

Statistical process control methods for monitoring processes with univariate ormultivariate measurements are used widely when the quality variables fit to known probabilitydistributions. Some processes, however, are better characterized by a profile or a function of qualityvariables. For each profile, it is assumed that a collection of data on the response variable along withthe values of the c...

Multivariate control charts are used for monitoring multiple series simultaneously, for the purpose of detecting shifts in the mean vector in any direction. In the context of disease outbreak detection, interest is in detecting only an increase in the process means. Two practical approaches for deriving directional Hotelling charts are Follmann’s correction and Testik and Runger’s quadratic pro...

INTEGRATION OF DATA MJNING ALGORITHMS AND CONTROL' CBARI'S FOR MULTIVARIATE AND AUTOCORRELATED PROCESSES WEERAWAT JITPITAKLERT, Ph.D. ,The University of Texas at Arlington, 2009 Supervising Professor: Seoung Bum Kim The objective of tllli3 dissertation is to integrate state-of-the-art data mining 3lgoritbms with statistical process control (SPC) tools to a.chieve efficient 'monitoring in multiv...