SRSaRa: A SaRa-Inspired Modification of Pettitt's Test for Non-Parametric Change-Point Detection
Issue Date
2024Keywords
Change-point DetectionMultiple Change-point Detection
Nonparametric
Pettitt's Test
SaRa
Single Change-point Detection
Advisor
Hao, NingMetadata
Show full item recordPublisher
The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Abstract
The Signed-Rank Screening and Ranking Algorithm or SRSaRa is a non-parametric change-point 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.Type
textElectronic Thesis
Degree Name
M.S.Degree Level
mastersDegree Program
Graduate CollegeStatistics