The change-point problem for dependent observations

  title={The change-point problem for dependent observations},
  author={Liudas Giraitis and Remigijus Leipus and Donatas Surgailis},
  journal={Journal of Statistical Planning and Inference},

Change-point tests under local alternatives for long-range dependent processes

We consider the change-point problem for the marginal distribution of subordinated Gaussian processes that exhibit long-range dependence. The asymptotic distributions of Kolmogorov-Smirnov- and

Bootstrapping the empirical distribution of a stationary process with change-point

: When detecting a change-point in the marginal distribution of a stationary time series, bootstrap techniques are required to determine critical values for the tests when the pre-change distribution

Change-point detection in the marginal distribution of a linear process

: The subject of this paper is the detection of a change in the marginal distribution of a stationary linear process. By considering the marginal distribution, the change-point model can

Detection of multiple change-points in multivariate time series

We consider the multiple change-point problem for multivariate time series, including strongly dependent processes, with an unknown number of change-points. We assume that the covariance structure of

Asymptotics for the Sequential Empirical Process and Testing for Distributional Change for Stationary Linear Models

Detecting a change in the structure of a time series is a classical statistical problem. Here we consider a short memory causal linear process Xi = ∑∞ j=0 ajξi−j, i = 1, · · · , n, where the

Incorporating a change-point estimator when bootstrapping the empirical distribution of a stationary process

  • B. IvanoffN. Weber
  • Mathematics, Economics
    Communications in Statistics - Theory and Methods
  • 2020
Abstract The moving block bootstrap can be used to determine critical values for test statistics used to detect a change-point in the marginal distribution of a stationary time series. We examine the

Testing for Change in Stochastic Volatility with Long Range Dependence

In this paper, change-point problems for long memory stochastic volatility models are considered. A general testing problem which includes various alternative hypotheses is discussed. Under the

Testing for Change Points in Time Series

This article considers the CUSUM-based (cumulative sum) test for a change point in a time series. In the case of testing for a mean shift, the traditional Kolmogorov–Smirnov test statistic involves a

Nonparametric Statistical Inference for Ergodic Processes

  • D. RyabkoB. Ryabko
  • Mathematics, Computer Science
    IEEE Transactions on Information Theory
  • 2010
In this work, a method for statistical analysis of time series is proposed, which is used to obtain solutions to some classical problems of mathematical statistics under the only assumption that the



Testing and estimating change-points in time series

  • D. Picard
  • Mathematics
    Advances in Applied Probability
  • 1985
The aim of this paper is to present a few techniques which may be useful in the analysis of time series when a failure is suspected. We present two categories of tests and investigate their

An efficiency result for the empirical characteristic function in stationary time-series models

It is shown under general conditions that arbitrarily high asymptotic efficiencies can be obtained when the parameters of a stationary time series are estimated by fitting the characteristic

Slowly decaying correlations, testing normality, nuisance parameters

Abstract Slowly decaying serial correlations can cause goodness-of-fit tests for a distribution to reject the null hypothesis with probability tending to one with increasing sample size. When the

Off-line statistical analysis of change-point models using non parametric and likelihood methods

We investigate the problem of testing for a change-point in stationary statistical models and of estimating the change parameters in an off-line framework. This paper provides asymptotic results

Statistical methods for data with long-range dependence

It is well known to applied statisticians and scientists that the assumption of independence is often not valid for real data. In particular, even when all precautions are taken to prevent


Generation and estimation of these models are considered and applications on generated and real data presented, showing potentially useful long-memory forecasting properties.

Change-point estimators in case of small disorders

Retrospective and sequential tests for a change in distribution based on kolmogorov-smirnov-type statistics

Test based on Kologorov-Smirnov-type statistics are proposed for testing for changes in distribution in a nonparametric setting. Both the retrospective and sequential detection settings are