Detecting the Change of Variance by Using Conditional Distribution with Diverse Copula Functions
We propose new method for detecting the change of variance by using conditional distribution with diverse copula functions. We generate the conditional asymmetric random transformed data by employing asymmetric copula function and apply the conditional transformed data to the cumulative sum control (CUSUM) statistics in order to detect the change point of conditional variance by measuring the average run length (ARL) of CUSUM control charts by using Monte Carlo simulation method. We show that the ARLs of change point of conditional variance by CUSUM are affected by the directional dependence by using the bivariate Gaussian copula beta regression (Kim and Hwang 2017).
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