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Consistent two‐stage multiple change‐point detection in linear models
Author(s) -
Jin Baisuo,
Wu Yuehua,
Shi Xiaoping
Publication year - 2016
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11282
Subject(s) - stage (stratigraphy) , consistency (knowledge bases) , selection (genetic algorithm) , refining (metallurgy) , point (geometry) , change detection , computer science , mathematics , statistics , algorithm , mathematical optimization , artificial intelligence , chemistry , geology , paleontology , geometry
A two‐stage procedure for simultaneously detecting multiple change‐points in linear models is developed. In the cutting stage, the change‐point problem is converted into a model selection problem so that a modern model selection method can be applied. In the refining stage, the change‐points obtained in the cutting stage are finalized via a refining method. Under mild conditions, consistency of the number of change‐point estimates is established. The new procedure is fast and accurate, as shown in simulation studies. Its applicability in real situations is demonstrated via well‐log and ozone data. The Canadian Journal of Statistics 44: 161–179; 2016 © 2016 Statistical Society of Canada