Defines the L² over Wasserstein space to equip random probability measures with inherited Riemannian geometry, enabling statistical convergence results and Bayesian posterior consistency in the Wasserstein topology.
Theory of Probability & Its Applications , volume=
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The paper derives that calibration-conditional coverage follows a Beta(k, n+1-k) law under continuous i.i.d. exchangeability and quantifies non-i.i.d. departures via Wasserstein distances on transported beta laws, yielding explicit bounds in scale-shift, clustered, and mixing regimes.
Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
Develops a sieve-based local projection estimator that recovers causal state-dependent impulse responses under linearity of conditional means in micro-macro panels, with valid inference.
Empirical Bernstein calibrated confidence intervals achieve nominal coverage up to small remainders and minimax-optimal widths for nonparametric regression and density estimation under local smoothness assumptions.
citing papers explorer
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$L^2$ over Wasserstein: Statistical Analysis for Optimal Transport
Defines the L² over Wasserstein space to equip random probability measures with inherited Riemannian geometry, enabling statistical convergence results and Bayesian posterior consistency in the Wasserstein topology.
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Conformal Prediction via Transported Beta Laws
The paper derives that calibration-conditional coverage follows a Beta(k, n+1-k) law under continuous i.i.d. exchangeability and quantifies non-i.i.d. departures via Wasserstein distances on transported beta laws, yielding explicit bounds in scale-shift, clustered, and mixing regimes.
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Causal inference with ordinal outcomes: copula-based identification, estimation and sensitivity analysis
Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
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Causal State-Dependent Local Projections
Develops a sieve-based local projection estimator that recovers causal state-dependent impulse responses under linearity of conditional means in micro-macro panels, with valid inference.
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Safe and Sharp Honest Inference for Nonparametric Estimation via Empirical Bernstein Calibration
Empirical Bernstein calibrated confidence intervals achieve nominal coverage up to small remainders and minimax-optimal widths for nonparametric regression and density estimation under local smoothness assumptions.