A distribution-free change-point monitoring scheme in high-dimensional settings with application to industrial image surveillance
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Existing monitoring tools for multivariate data are often asymptotically distribution-free, computationally intensive, or require a large stretch of stable data. Many of these methods are not applicable to 'high dimension, low sample size' scenarios. With rapid technological advancement, high-dimensional data has become omnipresent in industrial applications. We propose a distribution-free change point monitoring method applicable to high dimensional data. Through an extensive simulation study, performance comparison has been done for different parameter values, under different multivariate distributions with complex dependence structures. The proposed method is robust and efficient in detecting change points under a wide range of shifts in the process distribution. A real-life application illustrated with the help of high-dimensional image surveillance dataset.
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