A robust approach for estimating change-points in the mean of an \(\mathrm{AR}(1)\) process

DOI10.3150/15-BEJ782zbMath1378.62059arXiv1403.1958MaRDI QIDQ520705

S. Chakar, Céline Lévy-Leduc, Stephane Robin, Emilie Lebarbier

Publication date: 5 April 2017

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1403.1958

zbMATH Keywords

model selectiontime seriesautoregressive modelchange-pointsrobust estimation of the \(\mathrm{AR}(1)\) parameter

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Point estimation (62F10) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (12)

On optimal segmentation and parameter tuning for multiple change-point detection and inference ⋮ A comparison of single and multiple changepoint techniques for time series data ⋮ Short communication: Detecting possibly frequent change-points: wild binary segmentation 2 and steepest-drop model selection ⋮ Robust multiscale estimation of time-average variance for time series segmentation ⋮ Bump detection in the presence of dependency: does it ease or does it load? ⋮ Multiple change point detection and validation in autoregressive time series data ⋮ Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise ⋮ Estimation and inference of time-varying auto-covariance under complex trend: a difference-based approach ⋮ Robust algorithms for multiphase regression models ⋮ A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals ⋮ Changepoint detection in non-exchangeable data ⋮ Autocovariance estimation in the presence of changepoints

Uses Software

• R
• CRAN
• AR1seg

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