Feasibility study on the least square method for fitting non-Gaussian noise data

Wei Xu, Wen Chen and Yingjie Liang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 1917-1930

Abstract: This study is to investigate the feasibility of least square method in fitting non-Gaussian noise data. We add different levels of the two typical non-Gaussian noises, Lévy and stretched Gaussian noises, to exact value of the selected functions including linear equations, polynomial and exponential equations, and the maximum absolute and the mean square errors are calculated for the different cases. Lévy and stretched Gaussian distributions have many applications in fractional and fractal calculus. It is observed that the non-Gaussian noises are less accurately fitted than the Gaussian noise, but the stretched Gaussian cases appear to perform better than the Lévy noise cases. It is stressed that the least-squares method is inapplicable to the non-Gaussian noise cases when the noise level is larger than 5%.

Keywords: Least square method; Non-Gaussian noise; Lévy distribution; Stretched Gaussian distribution; Least square fitting (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:1917-1930

DOI: 10.1016/j.physa.2017.11.108

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