Detecting change-points in multidimensional stochastic processes

DOI10.1016/J.CSDA.2005.12.004zbMath1157.62481OpenAlexW2109964888MaRDI QIDQ1010538

Jan G. De Gooijer

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2005.12.004

zbMATH Keywords

likelihood ratiocovariance matricesbinary segmentationCramér-von Misescumulative sum

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: hypothesis testing (62M07)

Related Items (6)

Multiple break detection in the correlation structure of random variables ⋮ CORRELATIONS BETWEEN INSURANCE LINES OF BUSINESS: AN ILLUSION OR A REAL PHENOMENON? SOME METHODOLOGICAL CONSIDERATIONS ⋮ An ANOVA-type test for multiple change points ⋮ Multiple change-point detection of multivariate mean vectors with the Bayesian approach ⋮ Greedy Gaussian segmentation of multivariate time series ⋮ Change-Point Detection for Variance Piecewise Constant Models

Cites Work

• Inferences about the parameters of a time series model with changing variance
• Robust Methods for Detection of Shifts of the Innovation Variance of a Time Series
• Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance
• Testing and Locating Variance Changepoints with Application to Stock Prices
• K-Sample Analogues of the Kolmogorov-Smirnov and Cramer-V. Mises Tests
• Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time
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