Compositional time series data comprises of multivariate observations that at each time point are essentially proportions of a whole quantity. This kind of data occurs frequently in many disciplines such as economics, geology and ecology. Usual multivariate statistical procedures available in the literature are not applicable for the analysis of such data since they ignore the inherent constrai...

The analysis of multi-dimensional biomedical systems requires analysis techniques, which are able to deal with multivariate data consisting of both time series as well as point processes. Univariate and bivariate analysis techniques in the frequency domain for time series and point processes are established and investigated, although the number of investigations is strongly biased towards time ...

Flajolet and Odlyzko (1990) derived asymptotic formulae the coefficients of a class of univariate generating functions with algebraic singularities. These results have been extended to classes of multivariate generating functions by Gao and Richmond (1992) and Hwang (1996, 1998), in both cases by reducing the multivariate case to the univariate case. Pemantle and Wilson (2013) outlined new mult...

In various environmental studies multivariate spatial–temporal correlated data are involved, hence appropriate techniques to enhance space–time prediction are in great demand. An extension of multivariate spatial geostatistics to a spatio-temporal domain might be a straightforward task; nevertheless, up to now, little has been done in a multivariate spatial–temporal context. Modeling and predic...

Chemometrics Raman spectroscopy StreamLineTM imaging Tablet imaging Empty ModellingTM multivariate curve resolution (EM-MCR) The quantity of Raman spectroscopy data and the speed at which it can be collected when using fast imaging techniques have made the application of multivariate data analysis techniques – such as chemometrics – an invaluable and powerful tool. By Tim Smith at Renishaw plc ...