A robust method for shift detection in time series
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Date
2015
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Abstract
We present a robust test for change-points in time series which is based on
the two-sample Hodges-Lehmann estimator. We develop new limit theory for a class of
statistics based on the two-sample U-quantile processes, in the case of short range dependent
observations. Using this theory we can derive the asymptotic distribution of our test statistic
under the null hypothesis. We study the finite sample properties of our test via a simulation
study and compare the test with the classical CUSUM test and a test based on the Wilcoxon-
Mann-Whitney statistic.
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Keywords
change-point tests, functional central limit theorem, two-sample U-quantiles, two-sample U-process, two-sample U-statistics, weakly dependent data, time series, Hodges-Lehmann estimator, shift detection