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Found 6 papers, 1 papers with code
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
The goal of this work is to quantify this scalability from three key aspects: (i) empirical convergence rates; (ii) robustness to data contamination; and (iii) efficient computational methods.
Limit Distribution Theory for the Smooth 1-Wasserstein Distance with Applications
This result is then used to derive new empirical convergence rates for classic $W_1$ in terms of the intrinsic dimension.
Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs
We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints.
Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning.
Inference for high-dimensional exchangeable arrays
We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes.
Bootstrap inference for quantile-based modal regression
Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop two bootstrap methods, a novel pivotal bootstrap and the nonparametric bootstrap, for our conditional mode estimator.
Statistics Theory Methodology Statistics Theory
