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SRSaRa: A SaRa-Inspired Modification of Pettitt's Test for Non-Parametric Change-Point Detection
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SRSaRa: A SaRa-Inspired Modification of Pettitt's Test for Non-Parametric Change-Point Detection
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Dissertation
SRSaRa: A SaRa-Inspired Modification of Pettitt's Test for Non-Parametric Change-Point Detection
2024
Overview
The Signed-Rank Screening and Ranking Algorithm or SRSaRa is a non-parametric changepoint detection technique that is based on a SaRa-like process with a diagnostic function inspired by Pettitt’s test. Possessing two modes, ‘LM’ and ‘MAX’ for single and multiple change-point detection respectively, the SRSaRa is flexible and robust to outliers through its diagnostic function. The SRSaRa’s ‘MAX’ mode for single change-point detection outperforms Pettitt’s test in several scenarios while maintaining Type-I error control, while the SRSaRa’s ‘LM’ mode is capable of controlling FDR at the desired level and shows promise as a non-parametric multiple change-point detection technique.
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9798384023968
