Large-scale simultaneous testing using kernel density estimation

DOI10.1007/s13171-020-00220-5OpenAlexW3093266031MaRDI QIDQ2082348

Santu Ghosh, Alan M. Polansky

Publication date: 4 October 2022

Published in: Sankhyā. Series A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13171-020-00220-5

zbMATH Keywords

kernel density estimatorEdgeworth expansionfalse discovery ratetwo-sample t-test

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Parametric hypothesis testing (62F03)

Uses Software

• KernSmooth

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Smoothed and iterated bootstrap confidence regions for parameter vectors
• Phase transition and regularized bootstrap in large-scale \(t\)-tests with false discovery rate control
• Significance analysis of microarrays applied to the ionizing radiation response
• To How Many Simultaneous Hypothesis Tests Can Normal, Student'stor Bootstrap Calibration Be Applied?
• A stagewise rejective multiple test procedure based on a modified Bonferroni test
• A sharper Bonferroni procedure for multiple tests of significance
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
• A Direct Approach to False Discovery Rates
• The bootstrap and Edgeworth expansion
• Bandwidth selection for the smoothed bootstrap percentile method.