@article {18512,
	title = {A Pdf-free Change Detection Test Based on Density Difference Estimation},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {29},
	issue = {2},
	year = {2018},
	month = {11/2016},
	pages = { 324 - 334},
	chapter = {324},
	abstract = {The ability to detect 1 online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.},
	doi = {10.1109/TNNLS.2016.2619909},
	author = {Bo, Li and Alippi, Cesare and Zhao, Dongbin}
}
