Academic Journal

False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study
Συγγραφείς: Nguyen, Hien D., Yee, Yohan, McLachlan, Geoffrey J., Lerch, Jason P.
Πηγή: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Dipòsit Digital de Documents de la UAB
Universitat Autònoma de Barcelona
SORT-Statistics and Operations Research Transactions; 2019: Vol.: 43 Núm.: 2 July-December; 237-258
oai:raco.cat:article/361349
Repositori Institucional de la Universitat Rovira i Virgili
Universitat Rovira i virgili (URV)
Στοιχεία εκδότη: Institut d'Estadística de Catalunya, 2019.
Έτος έκδοσης: 2019
Θεματικοί όροι: Estadística matemàtica, grouped data, Discrete support, Classificació AMS::62 Statistics::62F Parametric inference, data quantization, Classificació AMS::62 Statistics::62P Applications, Package, Maximum-Likelihood, Choice, Models, Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica, Anàlisi de supervivència (Biometria), 62 Statistics::62F Parametric inference [Classificació AMS], Empirical-bayes, 2613 Statistics and Probability, 62 Statistics::62N Survival analysis and censored data [Classificació AMS], Projections, Mixture model, discrete support, 62 Statistics [Classificació AMS], Classificació AMS::62 Statistics, mixture model, Histogram, Stria Terminalis, Null Distribution, Estadística matemàtica--Aplicacions, Classificació AMS::62 Statistics::62N Survival analysis and censored data, Data quantization, Grouped data, 62 Statistics::62P Applications [Classificació AMS], 1803 Management Science and Operations Research, false discovery rate control, False discovery rate control, 1804 Statistics, empirical-Bayes, Matemàtiques i estadística::Estadística matemàtica [Àrees temàtiques de la UPC], Incompletely observed data, Censored data, Probability and Uncertainty, Networks, Bed Nuclei, incompletely observed data
Περιγραφή: False discovery rate (FDR) control is important in multiple testing scenarios that are common in neuroimaging experiments, and p-values from such experiments may often arise from some discretely supported distribution or may be grouped in some way. Two situations that may lead to discretely supported distributions are when the p-values arise from Monte Carlo or permutation tests are used. Grouped p-values may occur when p-values are quantized for storage. In the neuroimaging context, grouped p-values may occur when data are stored in an integer-encoded form. We present a method for FDR control that is applicable in cases where only p-values are available for inference, and when those p-values are discretely supported or grouped. We assess our method via a comprehensive set of simulation scenarios and find that our method can outperform commonly used FDR control schemes in various cases. An implementation to a mouse imaging data set is used as an example to demonstrate the applicability of our approach.
Τύπος εγγράφου: Article
Περιγραφή αρχείου: application/pdf; application/zip
Γλώσσα: English
DOI: 10.2436/20.8080.02.87
Σύνδεσμος πρόσβασης: http://hdl.handle.net/2117/362065
https://ddd.uab.cat/record/218269
http://hdl.handle.net/20.500.11797/RP4691
https://hdl.handle.net/2117/362065
https://espace.library.uq.edu.au/view/UQ:2d32f46/UQ2d32f46_OA.pdf
https://dialnet.unirioja.es/servlet/articulo?codigo=7214091
https://espace.library.uq.edu.au/view/UQ:2d32f46
Rights: CC BY NC ND
Αριθμός Καταχώρησης: edsair.dedup.wf.002..bbd2a0f39bdd5f7c53222a5cca3d0a2d
Βάση Δεδομένων: OpenAIRE