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זיהוי נקודות שינוי (PELT)×ניתוח סדרתי (תכנון סדרתי קבוצתי)×
תחוםסטטיסטיקהסטטיסטיקה
משפחהMachine learningHypothesis test
שנת המקור20121977
הוגה השיטהKillick, Fearnhead & EckleyP. C. O'Brien & T. R. Fleming; P. C. Pocock
סוגSequential segmentation algorithmSequential / adaptive hypothesis test
מקור מכונןKillick, 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 ↗
כינוייםStructural Break Detection, Breakpoint Analysis, Regime Change Detection, Değişim Noktası Tespitisequential testing, group sequential design, interim analysis, Sıralı Analiz (Sequential Testing / Group Sequential Design)
קשורות25
תקצירChange-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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ScholarGateהשוואת שיטות: Change-Point Detection · Sequential Analysis. אוחזר בתאריך 2026-06-15 מתוך https://scholargate.app/he/compare