K. Barbé, Lee Gonzales Fuentes, L. Barford, W. Moer
{"title":"Eliminating user-interaction in probability density estimation","authors":"K. Barbé, Lee Gonzales Fuentes, L. Barford, W. Moer","doi":"10.1109/I2MTC.2013.6555622","DOIUrl":null,"url":null,"abstract":"Despite the extensive literature, describing the probability content of measurements remains an important topic for engineering problems. The histogram remains the golden standard, even though kernel density estimation is a strong competitor when smooth estimates are desired. Critical user interaction is required for the use of both the histograms and kernel densities. A good choice for the bandwidth is essential in both cases. On top of that the kernel density method requires a proper choice of the kernel. Incorrect choices may lead to incorrect results generated by either masking important details or introducing false details. In this paper, we propose a new approach which requires no user-defined choices. The method is therefore fully automatic and provides the user a smooth density estimate of the probability content.","PeriodicalId":432388,"journal":{"name":"2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2MTC.2013.6555622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Despite the extensive literature, describing the probability content of measurements remains an important topic for engineering problems. The histogram remains the golden standard, even though kernel density estimation is a strong competitor when smooth estimates are desired. Critical user interaction is required for the use of both the histograms and kernel densities. A good choice for the bandwidth is essential in both cases. On top of that the kernel density method requires a proper choice of the kernel. Incorrect choices may lead to incorrect results generated by either masking important details or introducing false details. In this paper, we propose a new approach which requires no user-defined choices. The method is therefore fully automatic and provides the user a smooth density estimate of the probability content.