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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20311243 |
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| _version_ | 1866902216269365248 |
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| author | Ryan, Andrew |
| author_facet | Ryan, Andrew |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20311243 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Simulation Code for Dependence-Aware Lag-Resolved Correlation Analysis Ryan, Andrew time series permutation testing block permutation familywise error rate lag scanning Monte Carlo simulation dependence-aware inference <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> |
| title | Simulation Code for Dependence-Aware Lag-Resolved Correlation Analysis |
| topic | time series permutation testing block permutation familywise error rate lag scanning Monte Carlo simulation dependence-aware inference |
| url | https://doi.org/10.5281/zenodo.20311243 |