BAMIFun provides Bayesian multiple imputation for functional data via low-rank penalized spline models, achieving accurate imputation and improved coverage in simulations and real datasets compared to single-imputation FPCA methods.
Covariate balancing propensity score by tailored loss functions
2 Pith papers cite this work. Polarity classification is still indexing.
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Derives a closed-form task-specific strictly proper scoring rule for ATE estimation by matching local curvature of the IPW error metric.
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BAMIFun: Bayesian Multiple Imputation for Functional Data
BAMIFun provides Bayesian multiple imputation for functional data via low-rank penalized spline models, achieving accurate imputation and improved coverage in simulations and real datasets compared to single-imputation FPCA methods.
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Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Derives a closed-form task-specific strictly proper scoring rule for ATE estimation by matching local curvature of the IPW error metric.