2016
NBLDA: negative binomial linear discriminant analysis for RNA-Seq data
Dong K, Zhao H, Tong T, Wan X. NBLDA: negative binomial linear discriminant analysis for RNA-Seq data. BMC Bioinformatics 2016, 17: 369. PMID: 27623864, PMCID: PMC5022247, DOI: 10.1186/s12859-016-1208-1.Peer-Reviewed Original ResearchConceptsNegative binomial linear discriminant analysisNegative binomial distributionNegative binomial variablesPresence of overdispersionBinomial distributionSimulation resultsUnknown parametersNegative binomial modelBinomial modelReal RNA-seq data setsRNA-seq data classificationStatistical methodsDispersion parametersCount dataR codePoisson assumptionLinear discriminant analysisReal-world applicationsPoisson distributionImpact of dispersionDiscrete natureBinomial variablesComparison resultsBinomial classifierWittenFaster permutation inference in brain imaging
Winkler AM, Ridgway GR, Douaud G, Nichols TE, Smith SM. Faster permutation inference in brain imaging. NeuroImage 2016, 141: 502-516. PMID: 27288322, PMCID: PMC5035139, DOI: 10.1016/j.neuroimage.2016.05.068.Peer-Reviewed Original ResearchConceptsGamma distributionPermutation distributionProperties of statisticsReal data exampleInexpensive computing powerLinear modelGeneralised Pareto distributionMatrix theoryTail approximationNegative binomial distributionSymmetric errorsPareto distributionDirect fittingFamily-wise error ratePermutation inferenceReference resultsComplex modelsBinomial distributionReal dataSynthetic dataExact error rateComputing powerGeneral linear modelFamilywise errorNull hypothesis
2000
The maximum negative binomial distribution
Zhang Z, Burtness B, Zelterman D. The maximum negative binomial distribution. Journal Of Statistical Planning And Inference 2000, 87: 1-19. DOI: 10.1016/s0378-3758(99)00177-9.Peer-Reviewed Original Research
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