@misc{rakib_efficient_2026,
	title = {Efficient {Regression} {Models} for {Scan} {Statistics}},
	copyright = {Creative Commons Attribution 4.0 International},
	url = {https://arxiv.org/abs/2608.22201},
	doi = {10.48550/ARXIV.2608.22201},
	abstract = {We introduce a new class of regression models for scan statistics on real-valued signals. These allow for improved fitting of non-stationary signals to contrast with the interval anomalies identified by the scan statistics. Our models can represent generalized likelihood ratio statistics. While these methods naively require O(n4) for a length n signal, we provide algorithmic improvements which lead to linear time algorithms (with assumptions on max interval width). Our methods, especially ones based on Nadaraya-Watson kernel regression, are demonstrated as especially effective in detecting both synthetically planted anomalies, and for identifying a real “platforming” issue in interferometric astronomy.},
	language = {en},
	urldate = {2026-09-16},
	publisher = {arXiv},
	author = {Rakib, Gazi Abdur and Ashton, Tristan and Loomis, Ryan A. and Mason, Brian S. and Murphy, Eric J. and Xue, Ci and Phillips, Jeff M.},
	month = aug,
	year = {2026},
	note = {Version Number: 1},
	keywords = {FOS: Computer and information sciences, Machine Learning (cs.LG), Methodology (stat.ME)},
}

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