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dc.contributor.authorSilva, Ivair Ramos-
dc.contributor.authorZhuang, Yan-
dc.contributor.authorSilva Júnior, Júlio César Araújo da-
dc.identifier.citationSILVA, I. R.; ZHUANG, Y.; SILVA JUNIOR, J. C. A. da. Kronecker delta method for testing independence between two vectors in high-dimension. Statistical Papers, v. 63, p. 343-365, 2021. Disponível em: <>. Acesso em: 06 jul. 2022.pt_BR
dc.description.abstractConventional methods for testing independence between two Gaussian vectors require sample sizes greater than the number of variables in each vector. Therefore, adjustments are needed for the high-dimensional situation, where the sample size is smaller than the number of variables in at least one of the compared vectors. It is critical to emphasize that the methods available in the literature are unable to control the Type I error probability under the nominal level. This fact is evidenced through an inten- sive simulation study presented in this paper. To cover this lack, we introduce a valid randomized test based on the Kronecker delta covariance matrices estimator. As an empirical application, based on a sample of companies listed on the stock exchange of Brazil, we test the independence between returns of stocks of different sectors in the COVID-19 pandemic context.pt_BR
dc.subjectKronecker delta covariance structurept_BR
dc.subjectRandomized testingpt_BR
dc.subjectHigh-dimensional datapt_BR
dc.subjectMultivariate gaussian vectorspt_BR
dc.titleKronecker delta method for testing independence between two vectors in high-dimension.pt_BR
dc.typeArtigo publicado em periodicopt_BR
Appears in Collections:DEEST - Artigos publicados em periódicos

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