LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing

DOI10.1080/01621459.2020.1859379zbMath1506.62349OpenAlexW3112093669MaRDI QIDQ5881141

Wenguang Sun, Yin Xia, T. Tony Cai

Publication date: 9 March 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2020.1859379

zbMATH Keywords

false discovery ratedependent testsadjusted \(p\)-valuecovariate-assisted inferencestructured multiple testing

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Paired and multiple comparisons; multiple testing (62J15)

Related Items

Structure–Adaptive Sequential Testing for Online False Discovery Rate Control

Uses Software

• smoothfdr

Cites Work

• Unnamed Item
• A new approach to multiple testing of grouped hypotheses
• Microarrays, empirical Bayes and the two-groups model
• Cramér-type moderate deviation for the maximum of the periodogram with application to simultaneous tests in gene expression time series
• The control of the false discovery rate in multiple testing under dependency.
• Some results on false discovery rate in stepwise multiple testing procedures.
• Optimal weighting for false discovery rate control
• Unsupervised empirical Bayesian multiple testing with external covariates
• Multiple testing under dependence via graphical models
• Central limit theorems and bootstrap in high dimensions
• Power-enhanced multiple decision functions controlling family-wise error and false discovery rates
• On false discovery control under dependence
• Multiple testing for neuroimaging via hidden Markov random field
• Large-Scale Multiple Testing under Dependence
• Simultaneous Testing of Grouped Hypotheses: Finding Needles in Multiple Haystacks
• Multiple Testing with the Structure-Adaptive Benjamini–Hochberg Algorithm
• False discovery control with p-value weighting
• Changepoint estimation: another look at multiple testing problems
• Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control
• False Discovery Rates for Spatial Signals
• To How Many Simultaneous Hypothesis Tests Can Normal, Student'stor Bootstrap Calibration Be Applied?
• Hierarchical False Discovery Rate–Controlling Methodology
• Multiple Hypotheses Testing with Weights
• Empirical Bayes Analysis of a Microarray Experiment
• False Discovery Rate Smoothing
• Weighted False Discovery Rate Control in Large-Scale Multiple Testing
• Adaptive False Discovery Rate Control for Heterogeneous Data
• False Discovery Control for Random Fields
• A Direct Approach to False Discovery Rates
• Operating Characteristics and Extensions of the False Discovery Rate Procedure
• AdaPT: An Interactive Procedure for Multiple Testing with Side Information
• A general interactive framework for false discovery rate control under structural constraints
• GAP: A General Framework for Information Pooling in Two-Sample Sparse Inference
• Covariate-Assisted Ranking and Screening for Large-Scale Two-Sample Inference
• False Discovery Rate Control With Groups
• Multiple testing with heterogeneous multinomial distributions
• Correlation and Large-Scale Simultaneous Significance Testing
• The p-filter: Multilayer False Discovery Rate Control for Grouped Hypotheses
• False Discovery Control in Large-Scale Spatial Multiple Testing
• Detecting differential gene expression with a semiparametric hierarchical mixture method
• Selective Inference on Multiple Families of Hypotheses