2025
A Bayesian Approach to the G‐Formula via Iterative Conditional Regression
Liu R, Hu L, Wilson F, Warren J, Li F. A Bayesian Approach to the G‐Formula via Iterative Conditional Regression. Statistics In Medicine 2025, 44: e70123. PMID: 40476299, PMCID: PMC12184534, DOI: 10.1002/sim.70123.Peer-Reviewed Original ResearchConceptsCausal effect estimationTime-varying covariatesModel misspecification biasBayesian approachReal world data examplesG-formulaAverage causal effect estimationTime-varying treatmentsBayesian additive regression treesAverage causal effectAdditive regression treesConditional expectationOutcome regressionConditional distributionJoint distributionData examplesPosterior distributionMisspecification biasParametric regressionSimulation studyEffect estimatesSampling algorithmAlgorithm formulaCausal effectsFlexible machine learning techniques
2014
Estimation for General Birth-Death Processes
Crawford FW, Minin VN, Suchard MA. Estimation for General Birth-Death Processes. Journal Of The American Statistical Association 2014, 109: 730-747. PMID: 25328261, PMCID: PMC4196218, DOI: 10.1080/01621459.2013.866565.Peer-Reviewed Original ResearchBirth-death processGeneral birth–death processesConditional expectationE-stepEM algorithmLinear birth-death processContinuous-time Markov chainTransition probabilitiesClosed-form solutionLinear modelMaximum likelihood estimatesMaximum likelihood estimationTime-consuming simulationsStatistical inferenceCostly simulationsData augmentation procedureMarkov chainDiscrete timeEfficient computationLikelihood estimatesNumber of particlesFraction representationLaplace transformLikelihood estimationAlgorithm convergence
2006
On probabilistic properties of conditional medians and quantiles
Ghosh Y, Mukherjee B. On probabilistic properties of conditional medians and quantiles. Statistics & Probability Letters 2006, 76: 1775-1780. DOI: 10.1016/j.spl.2006.04.024.Peer-Reviewed Original Research
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