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Title: Detecting breaks in certain random intensities through sequential testing on point processes Authors:  Moinak Bhaduri - Bentley University (United States) [presenting]
Abstract: As we surface, probably momentarily, from the pandemic, other crises thwart normalcy: gross inequality, climate calamity, distressed refugees, and upped possibilities of a fresh Cold War. The enduring motif of our time is constant chaos. Frequently, that chaos results when one type of stationary system gives way to another. Change detection is mainly about estimating these points of deviation. Suppose a Poisson-type point process carries the system forward. In that case, we will offer a brand of detection algorithms, engineered through permutations of trend switched statistics and a judicious application of false discovery rate control. Certain members of this family that remain asymptotically consistent and close to the ground truth (evidenced through some Hausdorff-similarity) are isolated from pinpointing estimated change locations. Efficient forecasting proves to be a natural result. Change point-based clustering tools will also be examined. We will describe how such analyses offer concrete definitions to vague objects like Covid waves and measure their enormity.