Detecting change-points for shifts in mean and variance using fuzzy classification maximum likelihood change-point algorithms

DOI10.1016/j.cam.2016.06.006zbMath1381.62290OpenAlexW2424689621MaRDI QIDQ738998

Shao-Tung Chang, Kang-Ping Lu

Publication date: 16 August 2016

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2016.06.006

zbMATH Keywords

fuzzy clusteringmixture modelchange-pointcontrol chartfuzzy classification maximum likelihood change-point algorithm

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30) Non-Markovian processes: hypothesis testing (62M07) Multivariate analysis and fuzziness (62H86)

Related Items (2)

The optimized CUSUM and EWMA multi-charts for jointly detecting a range of mean and variance change ⋮ An assessment of the effect of using different mappings and Minkowski distances in joint monitoring of the time-between-event processes

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