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dc.contributor.authorSilva, Ivair Ramos-
dc.contributor.authorMaboudou-Tchao, Edgard M.-
dc.contributor.authorFigueiredo, Weslei Lima de-
dc.identifier.citationSILVA, I. R.; MABOUDOU-TCHAO, E. M.; FIGUEIREDO, W. L. de. Frequentist–Bayesian Monte Carlo test for mean vectors in high dimension. Journal of Computational and Applied Mathematics, v. 333, p. 51-64, maio 2018. Disponível em: <>. Acesso em: 16 jun. 2018.pt_BR
dc.description.abstractConventional methods for testing the mean vector of a P-variate Gaussian distribution require a sample size N greater than or equal to P. But, in high dimensional situations, that is when N is smaller than P, special and new adjustments are needed. Although Bayesianempirical methods are well-succeeded for testing in high dimension, their performances are strongly dependent on the actual unknown covariance matrix of the Gaussian random vector. In this paper, we introduce a hybrid frequentist–Bayesian Monte Carlo test and prove that: (i) under the null hypothesis, the performance of the proposed test is invariant with respect to the real unknown covariance matrix, and (ii) the decision rule is valid, which means that, in terms of expected loss, the performance of the proposed procedure can always be made as good as the exact Bayesian test and, in terms of type I error probability, the method is always of α level for arbitrary α ∈ (0, 1).pt_BR
dc.subjectInference in high dimensionpt_BR
dc.subjectHotelling’s testpt_BR
dc.subjectMonte Carlo testingpt_BR
dc.titleFrequentist–Bayesian Monte Carlo test for mean vectors in high dimension.pt_BR
dc.typeArtigo publicado em periodicopt_BR
Appears in Collections:DEEST - Artigos publicados em periódicos

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