Introduces Wasserstein Tangential PCA (WT-PCA) as a variational dynamical approach to log-PCA on the Wasserstein space and derives its empirical statistical convergence rate in 2-Wasserstein distance.
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces the MCB estimator for pointwise Wasserstein barycenter quantile estimation under sparse sampling by modeling the distribution of latent unit-level quantiles via marginal CDF distributions estimated with binomial mixtures, with consistency and asymptotic normality.
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Another Look at Log-PCA for Probability Measures: A Dynamical Formulation and Statistical Convergence
Introduces Wasserstein Tangential PCA (WT-PCA) as a variational dynamical approach to log-PCA on the Wasserstein space and derives its empirical statistical convergence rate in 2-Wasserstein distance.
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Estimating the Wasserstein barycenter of one-dimensional distributions under sparse sampling
Introduces the MCB estimator for pointwise Wasserstein barycenter quantile estimation under sparse sampling by modeling the distribution of latent unit-level quantiles via marginal CDF distributions estimated with binomial mixtures, with consistency and asymptotic normality.