{"title":"Correlation analysis of experimental permittivity data","authors":"K. Giese","doi":"10.1016/0001-8716(75)80022-8","DOIUrl":null,"url":null,"abstract":"<div><p>The auto-correlation function Φ<em><sub>h,h</sub></em>(<em>r</em>) of the relaxation time distribution function <em>h</em>(<em>r</em>) is obtained from the auto- and cross-correlations of real and imaginary parts of the permittivity by inversion of convolution integrals. If the permittivity data are subject to experimental error, the spectrum of the auto-correlation Φ<em><sub>h,h</sub></em>(<em>r</em>) appears to be most suitable for the determination of the main characteristics of the distribution function <em>h</em>(<em>r</em>). It is necessary to obtain additional information by evaluating the lower order moments of the distribution function.</p></div>","PeriodicalId":100050,"journal":{"name":"Advances in Molecular Relaxation Processes","volume":"7 3","pages":"Pages 157-166"},"PeriodicalIF":0.0000,"publicationDate":"1975-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0001-8716(75)80022-8","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Molecular Relaxation Processes","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0001871675800228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The auto-correlation function Φh,h(r) of the relaxation time distribution function h(r) is obtained from the auto- and cross-correlations of real and imaginary parts of the permittivity by inversion of convolution integrals. If the permittivity data are subject to experimental error, the spectrum of the auto-correlation Φh,h(r) appears to be most suitable for the determination of the main characteristics of the distribution function h(r). It is necessary to obtain additional information by evaluating the lower order moments of the distribution function.
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