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- W4205639276 abstract "Abstract The assumption of multivariate normality underlying the Hotelling chart is often violated for process data. The multivariate dependency structure can be separated from marginals with the help of copula theory, which permits to model association structures beyond the covariance matrix. Copula‐based estimation and testing routines have reached maturity regarding a variety of practical applications. We have constructed a rich design matrix for the comparison of the Hotelling chart with the copula test by Verdier and the copula test by Vuong, which allows for weighting the observations adaptively. Based on the design matrix, we have conducted a large and computationally intensive simulation study. The results show that the copula test by Verdier performs better than Hotelling in a large variety of out‐of‐control cases, whereas the weighted Vuong scheme often fails to provide an improvement." @default.
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- W4205639276 date "2022-01-21" @default.
- W4205639276 modified "2023-09-23" @default.
- W4205639276 title "Bivariate copula monitoring" @default.
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- W4205639276 doi "https://doi.org/10.1002/qre.3034" @default.
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