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Functional extensions of Mandel's h and k statistics for outlier detection in interlaboratory studies
dc.contributor.author | Flores, Miguel | |
dc.contributor.author | Tarrío-Saavedra, Javier | |
dc.contributor.author | Fernández-Casal, Rubén | |
dc.contributor.author | Naya, Salvador | |
dc.date.accessioned | 2024-06-27T09:01:17Z | |
dc.date.available | 2024-06-27T09:01:17Z | |
dc.date.issued | 2018-05-15 | |
dc.identifier.citation | Flores, M., Tarrío-Saavedra, J., Fernández-Casal, R., & Naya, S. (2018). Functional extensions of Mandel’s h and k statistics for outlier detection in interlaboratory studies. Chemometrics and Intelligent Laboratory Systems, 176, 134-148. https://doi.org/10.1016/J.CHEMOLAB.2018.03.016 | es_ES |
dc.identifier.issn | 0169-7439 | |
dc.identifier.uri | http://hdl.handle.net/2183/37468 | |
dc.description.abstract | [Abstract]: Functional data analysis (FDA) alternatives, based on the classical Mandel h and k statistics, are proposed to identify the laboratories that supply inconsistent results in interlaboratory studies (ILS). ILS is the procedure performed by a number of laboratories to test the precision of an analytical method, to measure the proficiency of laboratories in implementing an analytical procedure, to certify reference materials, and to evaluate a new experimental standard. The use of outlier tests, such as h and k Mandel statistics proposed by the ASTM E691, is crucial to assess these aims, estimating inter- and intra-laboratory data position and variability from a univariate point of view. Considering that experimental results obtained in analytical sciences are often functional, the use of FDA techniques can prevent the loss of important data information. The FDA approaches of h and k statistics are presented and point-wise obtained to deal with functional experimental data. Both functional statistics are estimated for each laboratory, their functional critical limits are obtained by bootstrap resampling, and new FDA versions of h and k graphics are presented. Real and synthetic thermogravimetric data are utilized to assess the good performance of the proposed FDA h and k statistics and their advantages with respect to the univariate approach. | es_ES |
dc.description.sponsorship | The research of Salvador Naya, Javier Tarrío-Saavedra and Rubén Fernández-Casal has been supported by MINECO grants MTM2014-52876- R and MTM2017-82724-R , and by the Xunta de Galicia ( Grupos de Referencia Competitiva ED431C-2016-015 and Centro Singular de Investigación de Galicia ED431G/01 ), all of them through the ERDF. The research of Miguel Flores has been partially supported by Grant PII - DM-002-2016 of Escuela Politécnica Nacional of Ecuador. The authors also thank Sonia Zaragoza, Fridama, Nerxus and ∑ qus companies their help in providing real data. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2016-015 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRI | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.chemolab.2018.03.016 | es_ES |
dc.rights | Attribution 4.0 International License (CC BY) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Bootstrap | es_ES |
dc.subject | Data depth | es_ES |
dc.subject | Functional data analysis | es_ES |
dc.subject | Interlaboratory studies | es_ES |
dc.subject | Outlier detection | es_ES |
dc.title | Functional extensions of Mandel's h and k statistics for outlier detection in interlaboratory studies | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Chemometrics and Intelligent Laboratory Systems | es_ES |
UDC.volume | 176 | es_ES |
UDC.startPage | 134 | es_ES |
UDC.endPage | 148 | es_ES |
dc.identifier.doi | 10.1016/j.chemolab.2018.03.016 |
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