On robustifying some second order blind source separation methods for nonstationary time series

被引:0
作者
Klaus Nordhausen
机构
[1] University of Tampere,School of Information Sciences and School of Health Sciences
来源
Statistical Papers | 2014年 / 55卷
关键词
Blind source separation; Joint diagonalisation; Nonstationarity; Robustness; Time series; 62M10; 60G35; 92C55;
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学科分类号
摘要
Blind source separation (BSS) is an important analysis tool in various signal processing applications like image, speech or medical signal analysis. The most popular BSS solutions have been developed for independent component analysis (ICA) with identically and independently distributed (iid) observation vectors. In many BSS applications the assumption on iid observations is not realistic, however, as the data are often an observed time series with temporal correlation and even nonstationarity. In this paper, some BSS methods for time series with nonstationary variances are discussed. We also suggest ways to robustify these methods and illustrate their performance in a simulation study.
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页码:141 / 156
页数:15
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