Classical multivariate statistical methods concern models, distributions and inference based on the Gaussian distribution. These are the topics in the first textbook for mathematical statisticians by T.W. Anderson that was published in 1958 and that appeared as a slightly expanded 3rd edition in 2003. Matrix theory and notation is used there extensively to efficiently derive properties of the m...

Process safety and environment pollution demands are continuously increasing in the process industry. Apart from that, requirements regarding final product quality and production efficiency are higher and higher [1]. This can be achieved by applying advanced process monitoring and control techniques. Process control is heavily dependent on the quality of the data, so it is crucial to measure as...

A method for the multivariate analysis of statistical phase synchronization phenomena in empirical data is presented. A first statistical approach is complemented by a stochastic dynamicmodel, to result in a data analysis algorithm which can in a specific sense be shown to be a generic multivariate statistical phase synchronization analysis. The method is applied to EEG data from a psychologica...

In this paper we describe multivariate statistical visualization techniques designed to improve the quality, accuracy and satisfaction of the statistical data analysis process. We describe techniques for visualizing multivariate data structure, for visualizing multivariate data models, and for visualizing multivariate data analysis sessions. We illustrate these techniques with ViSta, our statis...

In this paper we construct a modeling for detection of banks which are experiencing serious problems. Sample and variable set of the study contains 30 banks of Iran during 2006-2014 and their financial ratios. Well known multivariate statistical technique (principal component analysis) was used to explore the basic financial characteristics of the banks, and discriminant Logit and Probit models ...