We describe an extension of the pseudo maximum likelihood (PML) estimation method developed by Skinner (1989) to multistage strati ̄ed cluster sampling designs, including ̄nite population and unequal probability sampling. We conduct simulation studies to evaluate the performance of the proposed estimator. The estimator is also compared to the general estimating equation (GEE) method for linear r...

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In this paper, a brief outline of the evolution of the statistical techniques for the measurement of quality is firstly sketched. At the same time, the need for multivariate procedures is justified from both methodological and application points of view and a proposal of non parametric control charts is shown. The attention is then focused on visualising and monitoring quality. The peculiar fea...

Organizations often need to choose Software Architecture for future development from several competing candidate architectures. The various stakeholders’ quality requirements need to be considered collectively to describe the quality requirements of the envisioned system and therefore build the basis for the comparison and selection criteria. The involvement of many stakeholders with different ...

SUMMARY. Combination of {independent or dependent} tests for several multivariate hypotheses against restricted alternatives is considered. This includes a broad review of the classical Fisher method based on the observed significance levels (with necessary modifications). a variant form of the step-down procedure. a more plausible version of the Roy union-intersection principle and an extensio...

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...