Bootstrap Change Point Testing for Dependent Data

2018 ◽  
pp. 53-67 ◽  
Author(s):  
Zuzana Prášková
Keyword(s):  
Change Point ◽  
Dependent Data

scholarly journals Power of change-point tests for long-range dependent data

2017 ◽  
Vol 11 (1) ◽  
pp. 2168-2198 ◽  
Author(s):  
Herold Dehling ◽  
Aeneas Rooch ◽  
Murad S. Taqqu
Keyword(s):  
Long Range ◽  
Change Point ◽  
Dependent Data

scholarly journals Multiscale change point detection for dependent data

2020 ◽  
Vol 47 (4) ◽  
pp. 1243-1274
Author(s):  
Holger Dette ◽  
Theresa Eckle ◽  
Mathias Vetter
Keyword(s):  
Change Point ◽  
Change Point Detection ◽  
Dependent Data ◽  
Point Detection

scholarly journals Non-Parametric Change-Point Tests for Long-Range Dependent Data

2012 ◽  
Vol 40 (1) ◽  
pp. 153-173 ◽  
Author(s):  
HEROLD DEHLING ◽  
AENEAS ROOCH ◽  
MURAD S. TAQQU
Keyword(s):  
Long Range ◽  
Change Point ◽  
Dependent Data ◽  
Parametric Change ◽  
Non Parametric

Change point test for tail index for dependent data

Metrika ◽  
2010 ◽  
Vol 74 (3) ◽  
pp. 297-311 ◽  
Author(s):  
Moosup Kim ◽  
Sangyeol Lee
Keyword(s):  
Change Point ◽  
Tail Index ◽  
Dependent Data ◽  
Change Point Test ◽  
Point Test

CHANGE POINT TESTS FOR THE TAIL INDEX OF β-MIXING RANDOM VARIABLES

2016 ◽  
Vol 33 (4) ◽  
pp. 915-954 ◽  
Author(s):  
Yannick Hoga
Keyword(s):  
Simulation Study ◽  
Wide Class ◽  
Change Point ◽  
Tail Index ◽  
Dependent Data ◽  
Dependence Structure ◽  
Test Statistics ◽  
Finite Samples ◽  
Mixing Random Variables ◽  
Over Time

The tail index as a measure of tail thickness provides information that is not captured by standard volatility measures. It may however change over time. Currently available procedures for detecting those changes for dependent data (e.g., Quintos et al., 2001) are all based on comparing Hill (1975) estimates from different subsamples. We derive tests for a wide class of other tail index estimators. The limiting distribution of the test statistics is shown not to depend on the particular choice of the estimator, while the assumptions on the dependence structure allow for sufficient generality in applications. A simulation study investigates empirical sizes and powers of the tests in finite samples.

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