Analysis of heteroscedastic measurement data by the self-refining method of interval fusion with preference aggregation – IF&PA; Measurement; Vol. 183

Analysis of heteroscedastic measurement data by the self-refining method of interval fusion with preference aggregation – IF&PA; Measurement; Vol. 183

書目詳細資料
Parent link:	Measurement Vol. 183.— 2021.— [109851, 15 p.]
主要作者:	Muravyov (Murav’ev) S. V. Sergey Vasilyevich
企業作者:	Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники
其他作者:	Khudonogova L. I. Ludmila Igorevna, Ho Minh Dai
總結:	Title screen The newly proposed interval fusion with preference aggregation (IF&PA) procedure, further modified and formalized to improve the accuracy of the fusion result, is presented and applied to process the heteroscedastic data of a real experiment. The experiment consisted in determination of the reference values of the two measurands, DC voltage and resistance, based on the readings of five different models of multimeters. The data heteroscedasticity was ensured by a special transformation of the raw measurements. For comparison, the same data were processed by the method of weighted mean. For the two methods, absolute deviations of the obtained reference values from the nominal values (which are the high-precision calibrator outputs) and reference value uncertainties were estimated. The paper key outcome is that the IF&PA procedure provides a reference value to be very close to nominal value with considerably reduced uncertainty in comparison with traditional weighted mean calculation. Режим доступа: по договору с организацией-держателем ресурса
語言:	英语
出版:	2021
主題:	электронный ресурс труды учёных ТПУ interval fusion preference aggregation heteroscedasticity consensus estimate ranking kemeny rule
在線閱讀:	https://doi.org/10.1016/j.measurement.2021.109851
格式:	電子 Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666041

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