It has been proved that process capability indices provide very efficient measures of the capability of processes from many different perspectives. These indices have been widely used in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications. In the past few years, univariate capability indices have been introduced and used to characte...

Based on the concept of a Lévy copula to describe the dependence structure of a multivariate Lévy process we present a new estimation procedure. We consider a parametric model for the marginal Lévy processes as well as for the Lévy copula and estimate the parameters by a two-step procedure. We first estimate the parameters of the marginal processes, and then estimate in a second step only the d...

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the...

This paper deals with the problem of estimating the Multivariate version of the Conditional-TailExpectation, proposed by Cousin and Di Bernardino (2012). We propose a new non-parametric estimator for this multivariate risk-measure, which is essentially based on the Kendall’s process (see Genest and Rivest, 1993). Using the Central Limit Theorem for the Kendall’s process, proved by Barbe et al. ...

The results of a successful implementation of multivariate analysis on multiple fermentations are shown in the paper. To minimize batch-to-batch variability of an industrial fermentation process, multi-way partial least squares (PLS) was used. Eighteen baseline batches from different fermentors were analyzed. After applying diagnostics tools and contribution charts, three batches were clearly i...

Subspace method identification (SMI) and model reduction for Multivariate Statistical Process Control has been proposed as an improvement to dynamic principal component analysis (DPCA). The linear parametric model structure captures both static and dynamic information from the system. In this paper, an analysis of the dimension reduction capabilities of the subspace approach is provided. It is ...

Multiscale Principal Component Analysis (MSPCA) combines the ability of PCA to decorrelate the variables by extracting a linear relationship, with that of wavelet analysis to extract deterministic features and approximately decorrelate autocorrelated measurements. MSPCA computes the PCA of the wavelet coefficients at each scale, followed by combining the results at relevant scales. Due to its m...

We propose a new model for correlated outputs of mixed type, such as continuous and binary outputs, with a particular focus on joint regression and classification, motivated by an application in constrained optimization for computer simulation modeling. Our framework is based upon multivariate stochastic processes, extending Gaussian process methodology for modeling of continuous multivariate s...

Process capability indices show the ability of a process to produce products according to the pre-specified requirements. Since final quality characteristics of a product are usually interrelated to its previous amounts in earlier workstations, one need to model and consider the relationship among them to assess the process ca-pability properly. Hence, conducting process capability analysis in ...

The “predictor-corrector” feedback controller, a process adjustment scheme proposed for semiconductor manufacturing run-to-run processes that drift, is extended to the multiple input–multiple output case. The controller is based on two coupled multivariate exponentially-weighted-moving-average (EWMA) equations, thus its performance depends on the choices of EWMA weight matrices. Stability condi...