Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55981
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dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorIldar Batyrshinen_US
dc.contributor.authorVladik Kreinovichen_US
dc.date.accessioned2018-09-05T03:07:03Z-
dc.date.available2018-09-05T03:07:03Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn16860209en_US
dc.identifier.other2-s2.0-85008422675en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008422675&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55981-
dc.description.abstract© 2016 by the Mathematical Association of Thailand. All rights reserved. In many practical situations, we do not know the shape of the corresponding probability distributions and therefore, we need to use robust statistical techniques, i.e., techniques that are applicable to all possible distributions. Empirically, it turns out the the most efficient robust version of sample variance is the average value of the p-th powers of the deviations |xi- â|from the (estimated) mean â. In this paper, we use natural symmetries to provide a theoretical explanation for this empirical success, and to show how this optimal robust version of sample variance can be naturally extended to a robust version of sample covariance.en_US
dc.subjectMathematicsen_US
dc.titleWhich robust versions of sample variance and sample covariance are most appropriate for econometrics: Symmetry-based analysisen_US
dc.typeJournalen_US
article.title.sourcetitleThai Journal of Mathematicsen_US
article.volume14en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsInstituto Politecnico Nacionalen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
Appears in Collections:CMUL: Journal Articles

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