Gespeichert in:
| 1. Verfasser: | |
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| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2026
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.5281/zenodo.20311243 |
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Inhaltsangabe:
- <p>Python simulation code and reproducibility materials for the synthetic null simulations and block-length sensitivity analyses reported in:</p> <p>“Dependence-Aware Lag-Resolved Correlation Analysis in Multi-Sensor Stochastic Systems”.</p> <p>The repository reproduces the Monte Carlo experiments used to evaluate false-positive inflation under exploratory lag scanning with temporal dependence and the calibration behaviour of dependence-preserving block permutation surrogates under varying autocorrelation regimes.</p> <p>The simulations include:</p> <p>- AR(1) null processes with varying autocorrelation strengths<br>- exploratory lag scanning across symmetric lag domains<br>- max-statistic familywise error correction<br>- block-length sensitivity analyses<br>- comparison against naive uncorrected lag scanning</p> <p>All simulations were generated using a fixed master random seed:</p> <p>MASTER_SEED = 42</p> <p>This deposit accompanies the manuscript submitted to Algorithms (MDPI).</p>