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Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
Noisy matrix decomposition via convex relaxation high dimensions
2011/3/24
We analyze a class of estimators based on convex relaxation for solving high-dimensional matrix decomposition problems. The observations are the noisy realizations of the sum of an (appproximately) lo...
Simultaneous critical values for $t$-tests in very high dimensions
empirical processes FDR high dimension microarrays multiple hypothesis testing one-sample t-statistics self-normalized moderate deviation two-sample t-statistics
2011/3/21
This article considers the problem of multiple hypothesis testing using $t$-tests. The observed data are assumed to be independently generated conditional on an underlying and unknown two-state hidden...