Control chart is the main and powerful tool in statistical process control in order to detect and classify data, either in control or out of control. Its concept, basically, refers to the theory of prediction interval. Accordingly, in this paper, we aim at constructing of what so called predictive bivariate control charts, both classical and Copula-based ones. We argue that appropriate joint distribution function may be well estimated by employing Copula. A numerical analysis is carried out to illustrate that a Copula-based control chart outperforms than other.
REFERENCES
1.
Baillo
, A
and Cuevas
A.
, (2006
) Parametric versus Nonparametric Tolerance Region in Detection Problems
, Computational Statistics
21
(3-4
), 523
–536
2.
3.
Lestari
, T.
, (2014
) Bagan Kendali Bivariat Copula
, Unpublished MSc Thesis, Institut Teknologi Bandung
4.
Kabaila
, P
and Syuhada
, K.
, (2010
) The Asymptotic Efficiency of Improved Prediction Intervals
, Statistics and Probability Letters
80
(17-18
), 1348
–1353
5.
6.
Verdier
, G.
, (2013
) Application of Copulas to Multivariate Control Charts
, Journal of Statistical Planning and Inference
143
, 2151
–2159
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