Resampling Methods for the Change Analysis of Dependent Data

59 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Bootstrapping confidence intervals for the change‐point of time series (2007, methodology): “Thm B.8 (b) and Rem B.2 in Kirch (2006) give max kOm Ak1 Ak5 ¼ maxkOm an d 2n njn m kjmðn mÞ2 1ffiffiffi k p Xk i¼1 ðeðiÞ enÞ 2 ¼ o d 2n n 1 log n ¼ oð1Þ P a.s. Similarly, we obtain for an ¼ d 2n log n, where > 0 is an arbitrary fixed constant, 2008 The Authors Journal compilation 2008 Blackwell…”
TFT-bootstrap: Resampling time series in the frequency domain to obtain replicates in the time domain (2011, background): “1 in Kirch [29] for fairly general processes.”
Bootstrapping Sequential Change-Point Tests (2008, background): “Lemma D.1 in Kirch (2006; a generalization of the Hájek–Rényi inequality) remains true for an infinite maximum and an infinite sum (continuity arguments), the square-integrability of the martingale sequence is also not needed because the von Bahr–Esseen inequality remains true for martingales (cf. v”
On the detection of changes in autoregressive time series, II. Resampling procedures (2008, methodology): “In contrast to Kirch [18], who used blockwise resampling and resampling in the frequency domain to detect a change in a location model with errors generated by a linear process, we focus here on resampling methods based on residuals, which have been modified for our setup.”