Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation

Equity and bond market signals as leading indicators of bank fragility

We analyse the ability of the distance-to-default and bond spreads to signal bank fragility. We show that both indicators are complete and unbiased and that spreads are non-linear in the probability of bank default. We empirically test these properties in a sample of EU banks. We find leading properties for both indicators. The distance-to-default exhibits lead times of 6 to 18 months. Spreads have signal value close to default only, in line with the theory. We also find that implicit safety nets weaken the predictive power of spreads. Further, the results suggest complementarity between both indicators, reducing type I errors. We also examine the interaction of the indicators with other bank information. JEL Classification: G21, G12Bank fragility, banking, Market Indicators

Risk adjusted control charts for health care monitoring

Attribute data from high quality processes can be monitored effectively by deciding on whether or not to stop at each time where $r\geq 1$ failures have occurred. The smaller the degree of change in failure rate during Out-of-Control one wants to be optimally protected against, the larger $r$ should be. Under homogeneity, the distribution involved is negative binomial. However, in health care monitoring, (groups of) patients will often belong to different risk categories. In the present paper we will show how information about category membership can be used to adjust the basic negative binomial charts to the actual risk incurred. Attention is also devoted to comparing such conditional charts to their unconditional counterparts. The latter do take possible heterogeneity into account, but refrain from risk adjustment. Note that in the risk adjusted case several parameters are involved, which will typically all be unknown. Hence the potentially considerable estimation effects of the new charts will be investigated as well

A Nonparametric Multivariate Control Chart Based on Data Depth

For the design of most multivariate control charts, it is assumed that the observations follow a multivariate normal distribution. In practice, this assumption is rarely satisfied. In this work, a distribution-free EWMA control chart for multivariate processes is proposed. This chart is based on equential rank of data depth measures. --