Nonparametric Power-Law Surrogates
نویسندگان
چکیده
Power-law distributions are essential in computational and statistical investigations of extreme events complex systems. The usual technique to generate power-law distributed data is first infer the scale exponent $\alpha$ using observed interest then sample from associated distribution. This approach has important limitations because it relies on a fixed (e.g., limited applicability testing {\it family} distributions) hypothesis independent observations ignores temporal correlations other constraints typically present systems data). Here we propose constrained surrogate method that overcomes these by choosing uniformly at random set sequences exactly as likely be under discrete original sequence (i.e., regardless $\alpha$) showing how additional can imposed Markov transition probability between states). non-parametric involves redistributing prime factors randomize values accordance with model but without restricting ourselves or particular $\alpha$. We test our results simulated real data, ranging intensity earthquakes number fatalities disasters.
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ژورنال
عنوان ژورنال: Physical Review X
سال: 2022
ISSN: ['2160-3308']
DOI: https://doi.org/10.1103/physrevx.12.021056