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Monitoring Test for Stability of Dependence Structure in Multivariate Data Based on Copula

by Jiyeon Lee 1,† and Byungsoo Kim 2,*,†
1
Department of Statistics, Seoul National University, Seoul 08826, Korea
2
Department of Statistics, Yeungnam University, Gyeongsan 38541, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Adom Giffin
Entropy 2016, 18(12), 457; https://doi.org/10.3390/e18120457
Received: 17 September 2016 / Revised: 31 October 2016 / Accepted: 19 December 2016 / Published: 21 December 2016
In this paper, we consider a sequential monitoring procedure for detecting changes in copula function. We propose a cusum type of monitoring test based on the empirical copula function and apply it to the detection of the distributional changes in copula function. We investigate the asymptotic properties of the stopping time and show that under regularity conditions, its limiting null distribution is the same as the sup of Kiefer process. Moreover, we utilize the bootstrap method in order to obtain the limiting distribution. A simulation study and a real data analysis are conducted to evaluate our test. View Full-Text
Keywords: monitoring procedure; sequential test; copula function change; empirical copula function; Kiefer process monitoring procedure; sequential test; copula function change; empirical copula function; Kiefer process
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Lee, J.; Kim, B. Monitoring Test for Stability of Dependence Structure in Multivariate Data Based on Copula. Entropy 2016, 18, 457.

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