{"title":"重复时间序列中常见非线性的估计","authors":"A. G. Barnett, R. Wolff","doi":"10.1109/SSP.2001.955316","DOIUrl":null,"url":null,"abstract":"The bispectrum. is a higher-order statistic and is known to be a useful tool for detecting non-linearity. A succinct example of its power to identify non-linear sound waves from broken bridge struts was given by Rivola and White (1998). As well as detecting non-linearity it has the further advantage that its magnitude and shape can be used to estimate the third order non-linear structure (Barnett and Wolff). When a time series is repeated (such as sound waves from a collection of bridge struts) Diggle and Al-Wasel (1997) showed how to produce a common spectrum and to estimate individual departures from this global quantity. The purpose of this paper is to extend this method to the bispectrum and give a summary of common non-linearity among repeated time series. We evaluate our method using data from a group of people speaking the letter 'A' and from one person repeatedly speaking this letter.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"3 1","pages":"437-439"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"On the estimation of common non-linearity among repeated time series\",\"authors\":\"A. G. Barnett, R. Wolff\",\"doi\":\"10.1109/SSP.2001.955316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The bispectrum. is a higher-order statistic and is known to be a useful tool for detecting non-linearity. A succinct example of its power to identify non-linear sound waves from broken bridge struts was given by Rivola and White (1998). As well as detecting non-linearity it has the further advantage that its magnitude and shape can be used to estimate the third order non-linear structure (Barnett and Wolff). When a time series is repeated (such as sound waves from a collection of bridge struts) Diggle and Al-Wasel (1997) showed how to produce a common spectrum and to estimate individual departures from this global quantity. The purpose of this paper is to extend this method to the bispectrum and give a summary of common non-linearity among repeated time series. We evaluate our method using data from a group of people speaking the letter 'A' and from one person repeatedly speaking this letter.\",\"PeriodicalId\":70952,\"journal\":{\"name\":\"信号处理\",\"volume\":\"3 1\",\"pages\":\"437-439\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"信号处理\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2001.955316\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"信号处理","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/SSP.2001.955316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the estimation of common non-linearity among repeated time series
The bispectrum. is a higher-order statistic and is known to be a useful tool for detecting non-linearity. A succinct example of its power to identify non-linear sound waves from broken bridge struts was given by Rivola and White (1998). As well as detecting non-linearity it has the further advantage that its magnitude and shape can be used to estimate the third order non-linear structure (Barnett and Wolff). When a time series is repeated (such as sound waves from a collection of bridge struts) Diggle and Al-Wasel (1997) showed how to produce a common spectrum and to estimate individual departures from this global quantity. The purpose of this paper is to extend this method to the bispectrum and give a summary of common non-linearity among repeated time series. We evaluate our method using data from a group of people speaking the letter 'A' and from one person repeatedly speaking this letter.
期刊介绍:
Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.