CHANGE-POINT DETECTION WITH RANK STATISTICS IN LONG-MEMORY TIME-SERIES MODELS

DOI10.1111/j.1467-842X.2008.00515.xzbMath1337.62282MaRDI QIDQ2810355

Lihong Wang

Publication date: 1 June 2016

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

zbMATH Keywords

long-memory processrank statisticschange-point analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05) Order statistics; empirical distribution functions (62G30) Brownian motion (60J65) Non-Markovian processes: hypothesis testing (62M07)

Related Items (7)

Estimation methods for the LRD parameter under a change in the mean ⋮ Rank-based change-point analysis for long-range dependent time series ⋮ Non‐Parametric Change‐Point Tests for Long‐Range Dependent Data ⋮ Unsupervised segmentation of new semi-Markov chains hidden with long dependence noise ⋮ Testing for Change-Points in Long-Range Dependent Time Series by Means of a Self-Normalized Wilcoxon Test ⋮ Wilcoxon-Signed Rank Test for Long Memory Sequences ⋮ A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models

Uses Software

• longmemo

Cites Work

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• Detection of multiple changes in a sequence of dependent variables
• The effect of long-range dependence on change-point estimators
• Noncentral limit theorems and Appell polynomials
• On the asymptotic behavior of a class of nonparametric tests for a change-point problem
• Semiparametric analysis of long-memory time series
• Testing for change-points with rank and sign statistics
• Effect of dependence on statistics for determination of change
• Strong approximation for long memory processes with applications
• On the asymptotic expansion of the empirical process of long-memory moving averages
• Log-periodogram regression of time series with long range dependence
• Gaussian semiparametric estimation of long range dependence
• Averaged periodogram estimation of long memory
• The change-point problem for dependent observations
• Strong invariance principles for sequential Bahadur-Kiefer and Vervaat error processes of long-range dependent sequences
• Nonparametric change-point estimation for dependent sequences
• Change in non-parametric regression with long memory errors
• Bayesian methods for change-point detection in long-range dependent processes
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
• Long-Term Memory in Stock Market Prices
• Limit theorems in change-point problems with multivariate long-range dependent observations
• Testing and estimating for change in long memory parameter
• Change-Point Estimation in Long Memory Nonparametric Models with Applications
• Fractional Brownian Motions, Fractional Noises and Applications
• Gradual changes in long memory processes with applications

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