when a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. however, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always l...

In recent years, the monitoring of compositional data using control charts has been investigated in Statistical Process Control field. this study, we will design a Phase II Multivariate Exponentially Weighted Moving Average (MEWMA) chart with variable sampling intervals to monitor based on isometric log-ratio transformation. The Time Signal be computed Markov chain approach investigate performa...

abstract. in recent years several studies have shown that  control charts with adaptive schemes or double sampling plans detect both small and moderate shifts in the process mean more quickly than the traditional shewhart  chart. in the classical double sampling  chart, the difference between two points were placed in the central region of first stage was not considered. in this study, a new co...

Missing data is a common feature of large data sets in general and medical data sets in particular. Depending on the goal of statistical analysis, various techniques can be used to tackle this problem. Imputation methods consist in substituting the missing values with plausible or predicted values so that the completed data can then be analysed with any chosen data mining procedure. In this wor...

A multivariate control chart is proposed to detect changes in the process dispersion of multiple correlated quality characteristics. We focus on individual observations, where we monitor data vector-by-vector rather than (rational) subgroups. The developed by applying logarithm diagonal elements estimated covariance matrix. Then, this vector incorporated an exponentially weighted moving average...

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...