Methoden vergelijken

	Detectie van Veranderingspunten (PELT)	Sequentiële Analyse (Groepssequentiële Opzet)
Vakgebied	Statistiek	Statistiek
Familie	Machine learning	Hypothesis test
Jaar van ontstaan	2012	1977
Grondlegger	Killick, Fearnhead & Eckley	P. C. O'Brien & T. R. Fleming; P. C. Pocock
Type	Sequential segmentation algorithm	Sequential / adaptive hypothesis test
Oorspronkelijke bron	Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.	O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.
Aliassen	Structural Break Detection, Breakpoint Analysis, Regime Change Detection, Değişim Noktası Tespiti	sequential testing, group sequential design, interim analysis, Sıralı Analiz (Sequential Testing / Group Sequential Design)
Verwant	2	5
Samenvatting	Change-Point Detection identifies time points at which the statistical properties of a sequence — such as mean, variance, or distribution — shift abruptly. The Pruned Exact Linear Time (PELT) algorithm, introduced by Killick, Fearnhead, and Eckley (2012), solves the penalized segmentation problem exactly while achieving linear expected computational cost, making it practical for long time series encountered in genomics, finance, climatology, and signal processing.	Sequential analysis is a framework for conducting hypothesis tests with pre-planned interim looks at accumulating data, allowing a study to stop early for efficacy or futility while controlling the overall Type I error rate. The group sequential approach was formalised by Pocock (1977) and O'Brien and Fleming (1979), and remains the standard for confirmatory clinical trials and rigorous A/B experiments.