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Main Author: Ryan, Andrew
Format: Recurso digital
Language:English
Published: Zenodo 2026
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Online Access:https://doi.org/10.5281/zenodo.20311243
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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>
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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