2024
Nearest Neighbors of Multivariate Runs
Kong Y. Nearest Neighbors of Multivariate Runs. 2024, 275-299. DOI: 10.1007/978-1-4614-8033-4_63.Peer-Reviewed Original ResearchDoubly robust estimation and sensitivity analysis for marginal structural quantile models
Cheng C, Hu L, Li F. Doubly robust estimation and sensitivity analysis for marginal structural quantile models. Biometrics 2024, 80: ujae045. PMID: 38884127, DOI: 10.1093/biomtc/ujae045.Peer-Reviewed Original ResearchConceptsQuantile modelDistribution of potential outcomesEfficient influence functionPotential outcome distributionsDoubly robust estimatorsTime-varying treatmentsSequential ignorability assumptionSemiparametric frameworkIgnorability assumptionVariance estimationOutcome distributionInfluence functionRobust estimationPotential outcomesEfficient computationFunction approachTime-varying confoundersElectronic health record dataEstimationTreatment assignmentHealth record dataEffect of antihypertensive medicationEquationsRecord dataAntihypertensive medications
2023
Nearest Neighbors of Multivariate Runs
Kong Y. Nearest Neighbors of Multivariate Runs. 2023, 1-25. DOI: 10.1007/978-1-4614-8414-1_63-1.Peer-Reviewed Original Research
2020
A note on the estimation and inference with quadratic inference functions for correlated outcomes
Yu H, Tong G, Li F. A note on the estimation and inference with quadratic inference functions for correlated outcomes. Communications In Statistics - Simulation And Computation 2020, 51: 6525-6536. PMID: 36568127, PMCID: PMC9782733, DOI: 10.1080/03610918.2020.1805463.Peer-Reviewed Original ResearchQuadratic inference functionsInference functionScore equationsQuadratic inference function approachRegression parametersFinite samplesCombination of estimatorsGeneral settingEquationsCorrelated outcomesSimulation studyEstimatorFunction approachAnalytical insightsPopular methodInferenceSolutionMultiple setsMisspecificationSetFunctionEstimationAlternative solutionNoteParameters
2016
Number of appearances of events in random sequences: A new approach to non-overlapping runs
Kong Y. Number of appearances of events in random sequences: A new approach to non-overlapping runs. Communication In Statistics- Theory And Methods 2016, 45: 6765-6772. DOI: 10.1080/03610926.2014.968728.Peer-Reviewed Original Research
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