1,899 research outputs found

    Multivariate Statistical Process Control Charts: An Overview

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    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

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    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

    Reflective jacket

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    Safety product is created for workers, students, people and society to prevent from dangerous, harmful, injured also risks situation that can be occurs before, during and after works. The materials to produce the safety product must be strong, hard, good resistance, fast treatment and others characteristic that can be protect and prevent the user from the dangerous. Sometimes, the cost to produce the safety product is expensive because of the material to make the safety product become the good products

    Evaluating currency crises: A multivariate Markov regime switching approach

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    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

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    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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