A Effective Variance Change Detection Method under constantly Changing Mean
Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
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Keywords: Dual control windows, Variance change point detection, Smoothly changing mean trend, Subsampling, Liver procurement
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Abstract: Effectively evaluating the viability of a procured organ in the transplant patient prior the procedure is of critical importance. Current viability assessment methods rely on evaluating the organ’s morphology and/or laboratory biopsy results with limited effectiveness. A recently proposed, well-designed noninvasive method evaluated the viability status of organs by detecting the variance change point of their surface temperature through exploring the entire data profile. However, most part of the data in a temperature profile barely contains the change information, which yields a waste of computational resources of their method. This paper proposes an accelerating algorithm with a well-designed dual control windows scheme that can be extended to online change detection. The proposed method significantly improves the computational speed and retains the same change detection power as the method Gao19 through the removal of redundant data. Simulation and application results demonstrate the robust performance of the proposed method.
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Submission Number: 630
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