BILT generalizes the Diagonal Likelihood Ratio Test to block independence, establishes asymptotic normality under the null for high p and small n, and demonstrates higher power than DLRT in simulations and ADNI matrix-variate data.
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New Berry-Esseen bounds for multivariate martingale difference sequences achieve n^{-1/4} rate and polylog(d) dimension dependence in Kolmogorov distance.
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Block-Independent Likelihood Ratio Testing for High-Dimensional Mean Vectors with Applications to Matrix-Variate Data
BILT generalizes the Diagonal Likelihood Ratio Test to block independence, establishes asymptotic normality under the null for high p and small n, and demonstrates higher power than DLRT in simulations and ADNI matrix-variate data.
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Berry-Esseen bounds for multivariate martingale difference sequences in the Kolmogorov distance
New Berry-Esseen bounds for multivariate martingale difference sequences achieve n^{-1/4} rate and polylog(d) dimension dependence in Kolmogorov distance.