2022
A Sample Size Formula for Network Scale-up Studies
Josephs N, Feehan D, Crawford F. A Sample Size Formula for Network Scale-up Studies. Sociological Methods & Research 2022, 53: 1252-1289. DOI: 10.1177/00491241221122576.Peer-Reviewed Original ResearchSample size formulaRelative error propertySize formulaError propertiesPopulation size estimationNetwork scaleStatistical precisionSimple formMinimum sample sizeNetwork modelSample size calculationSample sizeNSUMSingle random sampleFormulaSize estimationSize calculationSubpopulations of interestEstimatorEstimationSampled individuals
2020
Estimating the size of a hidden finite set: Large-sample behavior of estimators
Cheng S, Eck D, Crawford F. Estimating the size of a hidden finite set: Large-sample behavior of estimators. Statistics Surveys 2020, 14: 1-31. DOI: 10.1214/19-ss127.Peer-Reviewed Original ResearchFinite setLarge sample behaviorStatistical propertiesAsymptotic propertiesAsymptotic regimeSampling probabilityHidden setStatistical approachStructural assumptionsIndirect statistical approachEstimatorDeterministic queriesDifferent modelsRandom samplingSetPopulation sizeSample increasesSample sizePropertiesRegimeModelProbabilityAssumptionSampling
2018
Confidence intervals for linear unbiased estimators under constrained dependence
Aronow P, Crawford F, Zubizarreta J. Confidence intervals for linear unbiased estimators under constrained dependence. Electronic Journal Of Statistics 2018, 12: 2238-2252. DOI: 10.1214/18-ejs1448.Peer-Reviewed Original ResearchLinear unbiased estimatorUnbiased estimatorWald-type confidence intervalsCentral limit theoremInteger linear programConservative coverageLimit theoremAlternative boundsHomoskedasticity assumptionLinear programSuch graphsDependency graphVariance estimatorIndependence relationsEstimatorGraphConstraintsTheoremBoundsDependent outcomesInferenceConfidence intervals
2016
Estimating the Size of a Large Network and its Communities from a Random Sample.
Chen L, Karbasi A, Crawford FW. Estimating the Size of a Large Network and its Communities from a Random Sample. Advances In Neural Information Processing Systems 2016, 29: 3072-3080. PMID: 28867924, PMCID: PMC5578631.Peer-Reviewed Original ResearchStochastic block modelMost real-world networksImportant global propertiesLarge networksNumber of verticesReal-world networksRandom graphsBlock membershipGlobal propertiesSize estimation algorithmPartial informationEstimation algorithmModel parametersBlock modelInduced subgraphTheoretical analysisGlobal network propertiesVerticesNetwork propertiesComputer scienceTotal degreeEstimatorNetworkGraphSample size