Vertaile menetelmiä
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| Muutoskohtien havaitseminen (PELT)× | Sekventiaalinen analyysi (ryhmäsekventiaalinen suunnittelu)× | |
|---|---|---|
| Tieteenala | Tilastotiede | Tilastotiede |
| Menetelmäperhe≠ | Machine learning | Hypothesis test |
| Syntyvuosi≠ | 2012 | 1977 |
| Kehittäjä≠ | Killick, Fearnhead & Eckley | P. C. O'Brien & T. R. Fleming; P. C. Pocock |
| Tyyppi≠ | Sequential segmentation algorithm | Sequential / adaptive hypothesis test |
| Alkuperäislähde≠ | 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. DOI ↗ | O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556. DOI ↗ |
| Rinnakkaisnimet | 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) |
| Liittyvät≠ | 2 | 5 |
| Tiivistelmä≠ | 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. |
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