A data-driven approach to detecting change points in linear regression models
From MaRDI portal
Publication:6626120
DOI10.1002/env.2591zbMATH Open1545.62854WikidataQ127751062 ScholiaQ127751062MaRDI QIDQ6626120
Tatiana V. Lebedeva, Vyacheslav Lyubchich, Jeremy M. Testa
Publication date: 28 October 2024
Published in: Environmetrics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- A MOSUM procedure for the estimation of multiple random change points
- Tests of stationarity against a change in persistence
- The limit distribution of the estimates in cointegrated regression models with multiple structural changes
- Testing for a change in persistence in the presence of non-stationary volatility
- Change-point in stochastic design regression and the bootstrap
- A local factor nonparametric test for trend synchronism in multiple time series
- Testing and dating of structural changes in practice
- Testing for changes in polynomial regression
- Distribution-free cumulative sum control charts using bootstrap-based control limits
- Optimal online detection of parameter changes in two linear models
- Tests for parameter changes at unknown times in linear regression models
- Asymptotic distribution of a statistic testing a change in simple linear regression with equidistant design.
- Residual partial sum limit process for regression models with applications to detecting parameter changes at unknown times
- Monitoring changes in linear models
- Bootstrapping sequential change-point tests for linear regression
- Testing for multiple change points
- Multiple changepoint detection with partial information on changepoint times
- On the detection of changes in autoregressive time series. II: Resampling procedures
- Extensions of some classical methods in change point analysis
- Bootstrap Procedures for Online Monitoring of Changes in Autoregressive Models
- Guaranteed Conditional Performance of Control Charts via Bootstrap Methods
- Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes
- Change‐point monitoring in linear models
- Bootstrapping confidence intervals for the change-point of time series
- Change detection in linear regression with time series errors
- Estimating Optimal Transformations for Multiple Regression and Correlation
- Change-point problem and bootstrap
- Testing For and Dating Common Breaks in Multivariate Time Series
- Using Difference-Based Methods for Inference in Nonparametric Regression with Time Series Errors
- Detecting at‐Most‐m Changes in Linear Regression Models
- Revisiting simple linear regression with autocorrelated errors
- Resampling in the frequency domain of time series to determine critical values for change-point tests
- Mean shift testing in correlated data
- The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes
- The Elements of Statistical Learning
- Can we weather proof our insurance?
This page was built for publication: A data-driven approach to detecting change points in linear regression models