We develop efficient methods for simulating processes of Ornstein–Uhlenbeck type related to the class p-tempered $$\alpha $$ -stable ( $$\textrm{TS}^p_\alpha ) distributions. Our results hold both univariate and multivariate cases we consider case where distribution is stationary law it background driving Lévy process. In latter case, also derive an explicit representation transition as this wa...

In this paper, we give a new covariation spectral representation of some non stationary symmetric α-stable processes (SαS). This representation is based on a weaker covariation pseudo additivity condition which is more general than the condition of independence. This work can be seen as a generalization of the covariation spectral representation of processes expressed as stochastic integrals wi...

In this work we address the integration of Engineering Process Control (EPC) and Statistical Process Control (SPC) in complex multivariate scenarios with dynamics (autocorrelation). There are essentially two different integrative approaches: one in which an SPC supervision mechanism is used, without interfering, with an EPC controller active continuously all the time, and another in which the S...

This dissertation presents some new results in stationary multivariate time series. The asymptotic properties of the sample autocovariance are established, that is, we derive a multivariate version of Bartlett’s Classic Formula. The estimation of the autocovariance function plays a crucial role in time series analysis, in particular for the identification problem. Explicit formula for vector au...