Open AccessRobust Shift Detection in Time-Varying Autoregressive Processes
Author(s) -
Roland Fried
Publication year - 2016
Publication title -
österreichische zeitschrift für statistik
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.