Estimating the Locations of Multiple Change Points in the Mean

Joe H. Sullivan
Additional contact information
Joe H. Sullivan: Mississippi State University, MSU

Computational Statistics, 2002, vol. 17, issue 2, No 9, 289-296

Abstract: Summary Detecting and estimating the number and locations of multiple change points is difficult. Sometimes a single-change method can locate multiple shifts by recursively dividing the data at the most likely location of a single shift. However, a single-change method may not detect the presence of multiple changes and may not accurately estimate a division point when multiple changes are present. The Schwarz information criterion offers a direct way to estimate the number of shifts, but the locations maximizing the likelihood function must be known for each possible number of shifts. The latter task is computationally infeasible for a realistic amount of data. This paper proposes a general algorithm for estimating the likelihood-maximizing locations and gives an example in which multiple changes are detected. The performance is evaluated using simulation and the proposed method is shown to be superior to the recursive application of a single shift procedure.

Keywords: Schwarz; Information; Criterion (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s001800200107 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:17:y:2002:i:2:d:10.1007_s001800200107

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s001800200107

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().