Robust Shift Detection in Time-Varying Autoregressive Processes | Zendy

Roland Fried | Zendy

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Robust Shift Detection in Time-Varying Autoregressive Processes

Author(s) -

Roland Fried

Publication year - 2016

Publication title -

austrian journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.342

H-Index - 9

ISSN - 1026-597X

DOI - 10.17713/ajs.v37i1.285

Subject(s) - autoregressive model , outlier , autoregressive–moving average model , star model , autoregressive integrated moving average , statistics , time series , econometrics , series (stratigraphy) , computer science , mathematics , data mining , geology , paleontology

Tests for shift detection in locally-stationary autoregressive time series are constructed which resist contamination by a substantial amount of outliers. Tests based on a comparison of local medians standardized by a highly robust estimate of the variability show reliable performance in a broad variety of situations if the thresholds are adjusted for possible autocorrelations.

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