Multiple testing for neuroimaging via hidden Markov random field

DOI10.1111/biom.12329zbMath1419.62446arXiv1404.1371OpenAlexW3123277272WikidataQ36084834 ScholiaQ36084834MaRDI QIDQ2803494

Robert Koeppe, Hai Shu, Bin Nan

Publication date: 4 May 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.1371

zbMATH Keywords

Ising modelfalse discovery rateAlzheimer's diseasepenalized likelihoodgeneralized expectation-maximization algorithmlocal significance index

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Applications of statistics to biology and medical sciences; meta analysis (62P10) Paired and multiple comparisons; multiple testing (62J15)

Related Items (10)

LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing ⋮ False Discovery Rate Smoothing ⋮ Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models ⋮ Bayesian hidden Markov models for dependent large-scale multiple testing ⋮ Statistical disease mapping for heterogeneous neuroimaging studies ⋮ Hidden Markov model in multiple testing on dependent count data ⋮ A peeling algorithm for multiple testing on a random field ⋮ Covariate-modulated large-scale multiple testing under dependence ⋮ Effect-size estimation using semiparametric hierarchical mixture models in disease-association studies with neuroimaging data ⋮ Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events

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