A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.
Biometrics , volume =
4 Pith papers cite this work. Polarity classification is still indexing.
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Unifying framework for CTree, MOB and GUIDE shows model scores without dichotomization yield higher power for covariate selection than residuals or dichotomized scores in many scenarios.
Extends nonparanormal adjusted marginal inference into a joint model that preserves marginal treatment effect interpretability while estimating heterogeneity for multiple outcome types.
MtFAD plus MOBSynC on GAMA data yields eight simple clusters that merge into red and blue sequences containing substructure tied to mass quenching, environmental quenching, morphology and environment.
citing papers explorer
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Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means
A Bayesian predictive model adaptively selects martingale factors to construct asymptotically log-optimal confidence sequences for bounded means while preserving anytime validity under misspecification.
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The Power of Unbiased Recursive Partitioning: A Unifying View of CTree, MOB, and GUIDE
Unifying framework for CTree, MOB and GUIDE shows model scores without dichotomization yield higher power for covariate selection than residuals or dichotomized scores in many scenarios.
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Joint Estimation of Marginal and Heterogeneous Treatment Effects
Extends nonparanormal adjusted marginal inference into a joint model that preserves marginal treatment effect interpretability while estimating heterogeneity for multiple outcome types.
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Multi-layered model-based characterisation of the local-Universe galaxy data from the GAMA survey
MtFAD plus MOBSynC on GAMA data yields eight simple clusters that merge into red and blue sequences containing substructure tied to mass quenching, environmental quenching, morphology and environment.