Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators

zbMath1318.62252arXiv1003.0747MaRDI QIDQ2933848

Pierre Neuvial

Publication date: 8 December 2014

Full work available at URL: https://arxiv.org/abs/1003.0747

zbMATH Keywords

criticalityconvergence ratesadaptive controlkernel estimatorsmultiple testingfalse discovery rateBenjamini-Hochberg procedureplug-in procedurestest statistics distribution

Mathematics Subject Classification ID

Density estimation (62G07) Paired and multiple comparisons; multiple testing (62J15)

Related Items (5)

On Efficient Estimators of the Proportion of True Null Hypotheses in a Multiple Testing Setup ⋮ A fast reduced kernel extreme learning machine ⋮ Covariate-Assisted Ranking and Screening for Large-Scale Two-Sample Inference ⋮ Adaptive \(p\)-value weighting with power optimality ⋮ New procedures controlling the false discovery proportion via Romano-Wolf's heuristic

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