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Lea Wegner

Publications and source records attributed to Lea Wegner.

3 recordsLinked to original sources

Functional Sieve Bootstrap for the Partial Sum Process with Application to Change-Point Detection

This paper applies the functional sieve bootstrap (FSB) to estimate the distribution of the partial sum process for time series stemming from a weakly stationary functional process. Consistency of the FSB procedure under weak assumptions on the underlying functional process is established. This result allows for the application of the FSB procedure to testing for a change-point in the mean of a functional time series using the CUSUM-statistic. We show that the FSB asymptotically correctly estimates critical values of the CUSUM-based test under the null-hypothesis. Consistency of the FSB-based test under local alternatives also is proven. The finite sample performance of the procedure is studied via simulations.

math.ST

Robust Change-Point Detection for Functional Time Series Based on $U$-Statistics and Dependent Wild Bootstrap

The aim of this paper is to develop a change-point test for functional time series that uses the full functional information and is less sensitive to outliers compared to the classical CUSUM test. For this aim, the Wilcoxon two-sample test is generalized to functional data. To obtain the asymptotic distribution of the test statistic, we proof a limit theorem for a process of $U$-statistics with values in a Hilbert space under weak dependence. Critical values can be obtained by a newly developed version of the dependent wild bootstrap for non-degenerate 2-sample $U$-statistics.

math.ST

Block Length Choice for the Bootstrap of Dependent Panel Data -- a Comment on Choi and Shin (2020)

Choi and Shin (2020) have constructed a bootstrap-based test for change-points in panels with temporal and and/or cross-sectional dependence. They have compared their test to several other proposed tests. We demonstrate that by an appropriate, data-adaptive choice of the block length, the change-point test by Sharipov, Tewes, Wendler (2016) can at least cope with mild temporal dependence, the size distortion of this test is not as severe as claimed by Choi and Shin (2020).

stat.ME