- Author
- Year
- 2006
- Title
- Detecting change-points in multidimensional stochatic processes
- Journal
- Computational Statistics and Data Analysis
- Volume | Issue number
- 51 | 3
- Pages (from-to)
- 1892-1903
- Number of pages
- 12
- Document type
- Article
- Faculty
- Faculty of Economics and Business (FEB)
- Institute
- Amsterdam School of Economics Research Institute (ASE-RI)
- Abstract
-
A general test statistic for detecting change-points in multidimensional stochastic processes with unknown parameters is proposed. The test statistic is specialized to the case of detecting changes in sequences of covariance matrices. Large-sample distributional results are presented for the test statistic under the null hypothesis of no-change. The finite-sample properties of the test statistic are compared with two other test statistics proposed in the literature. Using a binary segmentation procedure, the potential of the various test statistics is investigated in a multidimensional setting both via simulations and the analysis of a real life example. In general, all test statistics become more effective as the dimension increases, avoiding the determination of too many 'incorrect' change-point locations in a one-dimensional setting.
- URL
- go to publisher's site
- Language
- Undefined/Unknown
- Persistent Identifier
- https://hdl.handle.net/11245/1.270605
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