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Article Abstract
In this study, we construct surrogate stochastic processes that are challenging to distinguish from ordinary Brownian motion using a method based on the Schauder representation. Specifically, by assuming non-Gaussian (beta and uniform) distributions for the Schauder coefficients, we generate sample paths that preserve key properties of Brownian motion-such as quadratic variation, covariance structure, pointwise Hölder regularity, uncorrelated increments, as well as Gaussian marginal distributions. However, a deeper analysis relying on entropy-based measures and sliding-window spectral variance reveals that only the Gaussian-based construction preserves the expected randomness and the consistent spectral behavior of Brownian motion over time. In contrast, non-Gaussian variants exhibit subtle deviations from true Brownian motion.
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| http://dx.doi.org/10.1063/5.0287678 | DOI Listing |
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