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An Effective Variance Change Detection Method for Data With Constantly Changing Mean Function
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DOI：10.1002/sta4.70114.png)
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Abstract
En 中文
Effectively evaluating the viability of a procured organ in the transplant patient prior to 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 change detection 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 conventional method through the removal of redundant data. Simulation and application results demonstrate the robust performance of the proposed method.
Keywords:
Algorithms
Medical statistics
Penalized regression (Ridge, Lasso, ...)
Smoothing
Statistical modelling