Pages that link to "Item:Q3632593"

The following pages link to To How Many Simultaneous Hypothesis Tests Can Normal, Student's<i>t</i>or Bootstrap Calibration Be Applied? (Q3632593):

Displaying 38 items.

• Two sample tests for high-dimensional covariance matrices (Q150754) (← links)
• Self-normalized Cramér-type moderate deviations under dependence (Q309727) (← links)
• Econometric estimation with high-dimensional moment equalities (Q311648) (← links)
• Generalized \(F\) test for high dimensional linear regression coefficients (Q391594) (← links)
• Robust test for detecting a signal in a high dimensional sparse normal vector (Q434546) (← links)
• Inference and testing breaks in large dynamic panels with strong cross sectional dependence (Q503563) (← links)
• Simultaneous critical values for \(t\)-tests in very high dimensions (Q637106) (← links)
• Stein's method for nonlinear statistics: a brief survey and recent progress (Q900755) (← links)
• Cramér-type moderate deviation for the maximum of the periodogram with application to simultaneous tests in gene expression time series (Q973889) (← links)
• High-dimensional classification using features annealed independence rules (Q1000303) (← links)
• Self-normalization: taming a wild population in a heavy-tailed world (Q1650693) (← links)
• Bootstrap -- an exploration (Q1731214) (← links)
• A new perspective on robust \(M\)-estimation: finite sample theory and applications to dependence-adjusted multiple testing (Q1800789) (← links)
• Some optimality properties of FDR controlling rules under sparsity (Q1951159) (← links)
• A Cramér moderate deviation theorem for Hotelling's \(T^{2}\)-statistic with applications to global tests (Q1952452) (← links)
• Testing and estimation for clustered signals (Q2073225) (← links)
• Neyman's truncation test for two-sample means under high dimensional setting (Q2077453) (← links)
• Large-scale simultaneous testing using kernel density estimation (Q2082348) (← links)
• Refined Cramér-type moderate deviation theorems for general self-normalized sums with applications to dependent random variables and winsorized mean (Q2131251) (← links)
• Robust inference via multiplier bootstrap (Q2196240) (← links)
• Tests for covariance matrices in high dimension with less sample size (Q2252902) (← links)
• A feasible high dimensional randomization test for the mean vector (Q2317248) (← links)
• A two-sample test for high-dimensional data with applications to gene-set testing (Q2380090) (← links)
• Multiple testing via \(\mathrm{FDR}_L\) for large-scale imaging data (Q2429942) (← links)
• Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors (Q2443203) (← links)
• Marginal asymptotics for the ``large \(p\), small \(n\)'' paradigm: with applications to microarray data (Q2456007) (← links)
• Stepup procedures controlling generalized FWER and generalized FDR (Q2473069) (← links)
• Adaptive choice of the number of bootstrap samples in large scale multiple testing (Q2863962) (← links)
• RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs (Q3304859) (← links)
• Rejoinder (Q4648549) (← links)
• On simultaneous calibration of two-sample t-tests for high-dimension low-sample-size data (Q5155183) (← links)
• Multiple Testing When Many <i>p</i>-Values are Uniformly Conservative, with Application to Testing Qualitative Interaction in Educational Interventions (Q5242476) (← links)
• LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing (Q5881141) (← links)
• Heteroscedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing (Q5885127) (← links)
• Inference on the best policies with many covariates (Q6150530) (← links)
• Robust high-dimensional tuning free multiple testing (Q6183774) (← links)
• A robust false discovery rate controlling procedure using the empirical likelihood with a fast algorithm (Q6564323) (← links)
• Asymptotic false discovery control of the Benjamini-Hochberg procedure for pairwise comparisons (Q6661778) (← links)