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dc.contributor.authorLeiva, Ricardo
dc.contributor.authorRoy, Anuradha
dc.date.accessioned2022-02-24T14:50:15Z
dc.date.available2022-02-24T14:50:15Z
dc.date.issued2022-02-01
dc.identifierdoi: 10.3390/sym14020291
dc.identifier.citationSymmetry 14 (2): 291 (2022)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/803
dc.description.abstractAn extension of the D2 test statistic to test the equality of mean for high-dimensional and k-th order array-variate data using k-self similar compound symmetry (k-SSCS) covariance structure is derived. The k-th order data appear in many scientific fields including agriculture, medical, environmental and engineering applications. We discuss the property of this k-SSCS covariance structure, namely, the property of Jordan algebra. We formally show that our D2 test statistic for k-th order data is an extension or the generalization of the D2 test statistic for second-order data and for third-order data, respectively. We also derive the D2 test statistic for third-order data and illustrate its application using a medical dataset from a clinical trial study of the eye disease glaucoma. The new test statistic is very efficient for high-dimensional data where the estimation of unstructured variance-covariance matrix is not feasible due to small sample size.
dc.titleMean Equality Tests for High-Dimensional and Higher-Order Data with k-Self Similar Compound Symmetry Covariance Structure
dc.date.updated2022-02-24T14:50:16Z
dc.description.departmentManagement Science and Statistics


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