Process-Oriented Basis Representation (POBREP) is an effective method of multivariate statistical process control that is seldom used in manufacturing. Rather than monitoring individual process variables, POBREP seeks to identify specific causes of production problems and map those into a basis matrix. These patterns can then be monitored with single-variable SPC techniques or traditional multi...

When performing process monitoring, the classical approach of multivariate statistical process control (MSPC) explicitly assumes the normal operating conditions (NOC) to be distributed normally. If this assumption is not met, usually severe out-of-control situations are missed or incontrol situations can falsely be seen as out-of-control. Combining mixture modelling with MSPC (MM-MSPC) leads to...

In this paper, an approach of wavelet-based nonlinear PCA for statistical process monitoring is presented. The strategy utilizes the optimal wavelet decomposition in such a way that only approximation and the highest detail functions are used thus simplifying the overall structure and making the interpretation at each scale more meaningful. An orthogonal nonlinear PCA procedure is incorporated ...

With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Extensions of traditional univariate Shewhart, CUSUM and EWMA control charts to multivariate quality control situations are based on Hotelling's T 2 statist...

This work focuses on detecting outliers within large and very large datasets using a computationally efficient procedure. The algorithm uses Tukey’s biweight function applied on the dataset to filter out the effects of extreme values for obtaining appropriate location and scale estimates. Robust Mahalanobis distances for all data points are calculated using these location and scale estimates. A...

Why to read "yet another book" on statistical methods? This is a question that may arise among chemometricians, chemists, students or anyone using chemometric methods in their research or everyday routine work. However, this book is a very good contribution to a better understanding of statistics because it really captures the point of potential misunderstanding between chemists and mathematici...

Is there a method to predict the stock market? What factors determine if a company’s stock value will rise or fall in a given year? Using the multivariate statistical methods of principal component analysis and discriminant analysis, we aim to determine an accurate method for classifying a company’s stock as a good or a poor investment choice. Additionally, we will explore the possibilities for...

The misclassification error which is usually used in tests to compare classification algorithms, does not make a distinction between the sources of error, namely, false positives and false negatives. Instead of summing these in a single number, we propose to collect multivariate statistics and use multivariate tests on them. Information retrieval uses the measures of precision and recall, and s...

Performance prediction can play an important role in improving the efficiency of multicomputers in executing scalable parallel applications. An accurate model of program execution time must include detailed algorithmic and architectural characterizations. The exact values for critical model parameters such as message latency and cache miss penalty can often be dificult to determine. This resear...

This paper introduces web-based interactive Linked Micromap (LM) plots, a set of dynamic visualization methods that allows readers to interactively select variables and modify the different views to help reveal relationships among the study units. This methodology provided the foundation for web-based micromaps used by the National Cancer Institute (NCI). This illustrates the power of visualiza...