Multivariate Statistical Process Control (MSPC) seeks to monitor several quality characteristics simultaneously. However, it has limitations derived from its inability identify the source of special variation in process. In this research, a proposed model that does not have limitation is presented. paper, data two scenarios were used: (A) created by simulation and (B) random variable obtained a...

This paper proposes a multi-objective model for the economic-statistical design of the variable sample size and sampling interval multivariate exponentially weighted moving average control chart by using double warning lines. The Markov chain approach is used to obtain the statistical properties. We extend the Lorenzen and Vance cost function considering multiple assignable causes and multivari...

in many statistical process control applications, the quality of a process is characterized by a profile. a profile is a function in terms of one or more explanatory variables. in profile monitoring, one is interested to monitor the performance of a process or product using this functional relationship. control charts for monitoring nonparametric profiles are useful when the relationship is too...

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they either cannot deliver satisfactory detection performance or cannot c...

Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We ma...

Comparison of gene expression for two groups of individuals form an important subclass of microarray experiments. We study multivariate procedures, in particular use of Hotelling's T2 for discrimination between the groups with a special emphasis on methods based on few genes only. We apply the methods to data from an experiment with a group of atopic dermatitis patients compared with a control ...

Monitoring a process that suffers from data contamination using a traditional multivariate T 2 chart can lead to an excessive number of false alarms. This paper extends the diagnostic statistic technique of Davis and Adams [1] to the multivariate case. A traditional T 2 control chart augmented by a diagnostic statistic improves the work stoppage rates for contaminated multivariate data and main...