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Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review
In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrel...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378223/ https://www.ncbi.nlm.nih.gov/pubmed/37510017 http://dx.doi.org/10.3390/e25071070 |
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author | Miwakeichi, Fumikazu Galka, Andreas |
author_facet | Miwakeichi, Fumikazu Galka, Andreas |
author_sort | Miwakeichi, Fumikazu |
collection | PubMed |
description | In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-Sieve Bootstrap (ARSB), a resampling method based on AutoRegressive (AR) models that approximates the underlying data-generating process. The uPRB method accurately identifies variable interactions but fails to detect self-feedback in some variables. The TSB method, despite performing worse than uPRB, is unable to detect feedback between certain variables. The SB method gives consistent causality results, although its ability to detect self-feedback decreases, as the mean block width increases. The ARSB method shows superior performance, accurately detecting both self-feedback and causality across all variables. Regarding the analysis of the Impulse Response Function (IRF), only the ARSB method succeeds in detecting both self-feedback and causality in all variables, aligning well with the connectivity diagram. Other methods, however, show considerable variations in detection performance, with some detecting false positives and others only detecting self-feedback. |
format | Online Article Text |
id | pubmed-10378223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103782232023-07-29 Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review Miwakeichi, Fumikazu Galka, Andreas Entropy (Basel) Review In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-Sieve Bootstrap (ARSB), a resampling method based on AutoRegressive (AR) models that approximates the underlying data-generating process. The uPRB method accurately identifies variable interactions but fails to detect self-feedback in some variables. The TSB method, despite performing worse than uPRB, is unable to detect feedback between certain variables. The SB method gives consistent causality results, although its ability to detect self-feedback decreases, as the mean block width increases. The ARSB method shows superior performance, accurately detecting both self-feedback and causality across all variables. Regarding the analysis of the Impulse Response Function (IRF), only the ARSB method succeeds in detecting both self-feedback and causality in all variables, aligning well with the connectivity diagram. Other methods, however, show considerable variations in detection performance, with some detecting false positives and others only detecting self-feedback. MDPI 2023-07-17 /pmc/articles/PMC10378223/ /pubmed/37510017 http://dx.doi.org/10.3390/e25071070 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Miwakeichi, Fumikazu Galka, Andreas Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review |
title | Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review |
title_full | Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review |
title_fullStr | Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review |
title_full_unstemmed | Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review |
title_short | Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review |
title_sort | comparison of bootstrap methods for estimating causality in linear dynamic systems: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378223/ https://www.ncbi.nlm.nih.gov/pubmed/37510017 http://dx.doi.org/10.3390/e25071070 |
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