TY  - JOUR
A1  - Easton, Andrew
A1  - van Dalen, Okki
A1  - Goeb, Rainer
A1  - Di Bucchianico, Alessandro
T1  - Bivariate copula monitoring
JF  - Quality and Reliability Engineering International
N2  - The assumption of multivariate normality underlying the Hotelling T\(^{2}\) 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 T\(^{2}\) 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 T\(^{2}\) in a large variety of out‐of‐control cases, whereas the weighted Vuong scheme often fails to provide an improvement.
KW  - copula
KW  - multivariate Gaussian distribution
KW  - multivariate statistical process control (SPC)
KW  - phase I
KW  - phase II
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-276501
VL  - 38
IS  - 3
SP  - 1272
EP  - 1288
ER  -