1,899 research outputs found
Multivariate Statistical Process Control Charts: An Overview
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 construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS
Multivariate control charts based on Bayesian state space models
This paper develops a new multivariate control charting method for vector
autocorrelated and serially correlated processes. The main idea is to propose a
Bayesian multivariate local level model, which is a generalization of the
Shewhart-Deming model for autocorrelated processes, in order to provide the
predictive error distribution of the process and then to apply a univariate
modified EWMA control chart to the logarithm of the Bayes' factors of the
predictive error density versus the target error density. The resulting chart
is proposed as capable to deal with both the non-normality and the
autocorrelation structure of the log Bayes' factors. The new control charting
scheme is general in application and it has the advantage to control
simultaneously not only the process mean vector and the dispersion covariance
matrix, but also the entire target distribution of the process. Two examples of
London metal exchange data and of production time series data illustrate the
capabilities of the new control chart.Comment: 19 pages, 6 figure
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Evaluating currency crises: A multivariate Markov regime switching approach
This paper provides an empirical framework to analyse the nature of currency crises byextending earlier work of Jeanne and Masson (2000) who suggest that a currency crisismodel with multiple equilibria can be estimated using Markov regime switching (MRS)models. However, Jeanne and Masson (2000) assume that the transition probabilitiesacross equilibria are constant and independent of fundamentals. Thus, currency crisis isdriven by a sunspot unrelated to fundamentals. This paper further contributes to theliterature by suggesting a multivariate MRS model to analyse the nature of currencycrises. In the new set up, one can test for the impact of the unobserved dynamics offundamentals on the probability of devaluation. Empirical evidence shows thatexpectations about fundamentals, which are reflected by their unobserved state variables,not only affect the probability of devaluation but also can be used to forecast a currencycrisis one period ahead
Bivariate modified hotelling’s T2 charts using bootstrap data
The conventional Hotelling’s charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively. The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested
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