Detection of multiple change-points in multivariate data

Edgard M. Maboudou-Tchao, Douglas M. Hawkins

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


The statistical analysis of change-point detection and estimation has received much attention recently. A time point such that observations follow a certain statistical distribution up to that point and a different distribution - commonly of the same functional form but different parameters after that point - is called a change-point. Multiple change-point problems arise when we have more than one change-point. This paper develops a method for multivariate normally distributed data to detect change-points and estimate within-segment parameters using maximum likelihood estimation.

Original languageEnglish (US)
Pages (from-to)1979-1995
Number of pages17
JournalJournal of Applied Statistics
Issue number9
StatePublished - Sep 2013


  • binary splitting
  • dynamic programming
  • principle of optimality
  • regression trees
  • separability


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