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作者：Li, Jun; Chen, Song Xi
作者单位：Iowa State University; Peking University; Peking University
摘要：We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance covariance matrices, and the other is on off-diagonal sub-matrices, which define the covariance between two nonoverlapping segments of the high-dimensional random vectors. The tests are applicable (i) when the data dimension is much larger than the sample sizes, namely the large p, small n situations and (ii) without assuming parametric distributions for the ...
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作者：Tang, Yu; Xu, Hongquan; Lin, Dennis K. J.
作者单位：Soochow University - China; University of California System; University of California Los Angeles; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：The minimum aberration criterion has been frequently used in the selection of fractional factorial designs with nominal factors. For designs with quantitative factors, however, level permutation of factors could alter their geometrical structures and statistical properties. In this paper uniformity is used to further distinguish fractional factorial designs, besides the minimum aberration criterion. We show that minimum aberration designs have low discrepancies on average. An efficient method ...
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作者：Zhu, Hongtu; Ibrahim, Joseph G.; Cho, Hyunsoon
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Cook's distance [Technometrics 19 (1977) 15-18] is one of the most important diagnostic tools for detecting influential individual or subsets of observations in linear regression for cross-sectional data. However, for many complex data structures (e.g., longitudinal data), no rigorous approach has been developed to address a fundamental issue: deleting subsets with different numbers of observations introduces different degrees of perturbation to the current model fitted to the data, and the ma...
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作者：Morgan, Kari Lock; Rubin, Donald B.
作者单位：Duke University; Harvard University
摘要：Randomized experiments are the gold standard for estimating causal effects, yet often in practice, chance imbalances exist in covariate distributions between treatment groups. If covariate data are available before units are exposed to treatments, these chance imbalances can be mitigated by first checking covariate balance before the physical experiment takes place. Provided a precise definition of imbalance has been specified in advance, unbalanced randomizations can be discarded, followed by...
