2019
Accelerated estimation and permutation inference for ACE modeling
Chen X, Formisano E, Blokland G, Strike L, McMahon K, de Zubicaray G, Thompson P, Wright M, Winkler A, Ge T, Nichols T. Accelerated estimation and permutation inference for ACE modeling. Human Brain Mapping 2019, 40: 3488-3507. PMID: 31037793, PMCID: PMC6680147, DOI: 10.1002/hbm.24611.Peer-Reviewed Original ResearchConceptsPermutation inferenceIterative optimizationVariance component modelComputation timeComparable biasAccelerated estimationLinear modelSquared errorCluster sizeSpatial statisticsLinear regression modelsFalse positive riskInferenceHeritability estimationEstimationComponent modelModelWealth of toolsMemory datasetsOptimizationSimple methodSmall numberStatisticsACE modelPermutations
2018
Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes
Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Donohue B, Kochunov P, Nichols TE. Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes. Nature Communications 2018, 9: 3254. PMID: 30108209, PMCID: PMC6092439, DOI: 10.1038/s41467-018-05444-6.Peer-Reviewed Original Research
2015
Multi-level block permutation
Winkler AM, Webster MA, Vidaurre D, Nichols TE, Smith SM. Multi-level block permutation. NeuroImage 2015, 123: 253-268. PMID: 26074200, PMCID: PMC4644991, DOI: 10.1016/j.neuroimage.2015.05.092.Peer-Reviewed Original ResearchFast and powerful heritability inference for family-based neuroimaging studies
Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Kochunov P, Nichols TE. Fast and powerful heritability inference for family-based neuroimaging studies. NeuroImage 2015, 115: 256-268. PMID: 25812717, PMCID: PMC4463976, DOI: 10.1016/j.neuroimage.2015.03.005.Peer-Reviewed Original ResearchConceptsAuxiliary linear modelSemi-parametric inferenceInference methodsP-value computationParametric inference methodsNovel inference methodSum of squaresMultiple testing problemLikelihood computationInference proceduresFast estimationStandard resultsComputational intensityData settingTesting problemLinear modelPermutation schemeMethodological resultsAnisotropy measuresSignificance testsComputationInferenceWald testFalse positive riskHeritability estimation
2014
Permutation inference for the general linear model
Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage 2014, 92: 381-397. PMID: 24530839, PMCID: PMC4010955, DOI: 10.1016/j.neuroimage.2014.01.060.Peer-Reviewed Original ResearchConceptsPermutation inferenceNon-standard statisticsComplex general linear modelArbitrary experimental designsLinear modelPermutation methodOnly weak assumptionsGLM parametersWeak assumptionsSymmetric distributionExact controlGeneral linear modelNuisance variablesInferenceDetailed exampleComplete algorithmAlgorithmExperimental designIndependent dataNuisance effectsUseful caseGeneric frameworkModelStatisticsGLM
2005
Tendência da mortalidade por doenças isquêmicas do coração na cidade de Curitiba - Brasil, de 1980 a 1998
Daniel E, Germiniani H, Nazareno E, Braga S, Winkler A, Cunha C. Tendência da mortalidade por doenças isquêmicas do coração na cidade de Curitiba - Brasil, de 1980 a 1998. Arquivos Brasileiros De Cardiologia 2005, 85: 100-104. PMID: 16113847, DOI: 10.1590/s0066-782x2005001500005.Peer-Reviewed Original ResearchConceptsIschemic heart diseaseAcute myocardial infarctionYears of ageMyocardial infarctionHeart diseaseAge rangeMortality rateData of deathMale sexIschemic diseasesMinistério da SaúdeInfarctionMortality trendsDiseaseGreater decreaseSexAgeTrend of reductionCity of CuritibaPopulation dataYearsSignificance levelPopulation of CuritibaLinear regressionMortalidade