Heavy-tailed prediction error: a difficulty in predicting biomedical signals of \(1/f\) noise type
type: Item
instance of: scholarly article
title: Heavy-tailed prediction error: a difficulty in predicting biomedical signals of \(1/f\) noise type
zbMATH Open document ID: 1261.92029
DOI: 10.1155/2012/291510
author: Ming Li
author: Wei Zhao
author: Biao Chen
published in: Computational \& Mathematical Methods in Medicine
publication date: 2013-01-09T00:00:00Z
review text: Summary: A fractal signal \(x(t)\) in biomedical engineering may be characterized by \(1/f\) noise, that is, the power spectrum density (PSD) divergences at \(f = 0\). According to Taqqu's law, \(1/f\) noise has the properties of long-range dependence and heavy-tailed probability density functions (PDFs). The contribution of this paper is to exhibit that the prediction error of a biomedical signal of \(1/f\) noise type is long-range dependent (LRD). Thus, it is heavy-tailed and of \(1/f\) noise. Consequently, the variance of the prediction error is usually large or may not exist, making predicting biomedical signals of \(1/f\) noise type difficult.
Mathematics Subject Classification ID: 92C55
zbMATH DE Number: 6123538
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/291510
OpenAlex ID: W2023304405
Wikidata QID: Q36464390
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DBLP publication ID: journals/cmmm/LiZC12
description: scientific article; zbMATH DE number 6123538