Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/11457
Title: Frequentist–Bayesian Monte Carlo test for mean vectors in high dimension.
Authors: Silva, Ivair Ramos
Maboudou-Tchao, Edgard M.
Figueiredo, Weslei Lima de
Keywords: Inference in high dimension
Monte Carlo testing
Hotelling’s test
Issue Date: 2019
Citation: SILVA, I. R.; MABOUDOU-TCHAO, E. M.; FIGUEIREDO, W. L. de. Frequentist–Bayesian Monte Carlo test for mean vectors in high dimension. Communications in Statistics-Theory and Methods, v. 333, p. 51-64, maio 2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/S037704271730523X>. Acesso em: 19 mar. 2019.
Abstract: Conventional 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).
URI: http://www.repositorio.ufop.br/handle/123456789/11457
metadata.dc.identifier.uri2: https://www.sciencedirect.com/science/article/pii/S037704271730523X
metadata.dc.identifier.doi: https://doi.org/10.1016/j.cam.2017.10.022
ISSN: 0377-0427
Appears in Collections:DEEST - Artigos publicados em periódicos

Files in This Item:
File Description SizeFormat 
ARTIGO_FrequentistBayesianMonte.pdf337,4 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.