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Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

Loi logistique

Graph Chatbot

Chattez avec Graph Search

Investigating Crosstalk Among PTMs Provides Novel Insight Into the Structural Basis Underlying the Differential Effects of Nt17 PTMs on Mutant Httex1 Aggregation

Multiple testing with test statistics following heavy-tailed distributions

Sub‐asymptotic motivation for new conditional multivariate extreme models

Efficient ensemble summaries are inversely related to visual crowding

Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

Blockage Probability Modeling of Large Wood at Reservoir Spillways With Piers

Hand pose selection in a bimanual fine-manipulation task

Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks

Robustness via Cross-Domain Ensembles

Chirok: a post-refinement tool to analyse absolute structure

Efficient ensemble summaries are inversely related to visual crowding

Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks

Robustness via Cross-Domain Ensembles

Investigating Crosstalk Among PTMs Provides Novel Insight Into the Structural Basis Underlying the Differential Effects of Nt17 PTMs on Mutant Httex1 Aggregation

Multiple testing with test statistics following heavy-tailed distributions

Sub‐asymptotic motivation for new conditional multivariate extreme models

Blockage Probability Modeling of Large Wood at Reservoir Spillways With Piers

Hand pose selection in a bimanual fine-manipulation task

Chirok: a post-refinement tool to analyse absolute structure

Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)