<p>We consider random i.i.d. samples of absolutely continuous measures on bounded connected domains. We prove an upper bound on the ∞-transportation distance between the measure and the empirical measure of the sample. The bound is optimal in terms of scaling with the number of sample points.</p
This paper is focused on the statistical analysis of probability measures $\bnu_{1},\ldots,\bnu_{n}$...
We consider a situation where two sample sets of independent real valued observations are obtained f...
Abstract. This note reviews, compares and contrasts three notions of “dis-tance ” or “size ” that ar...
Abstract. We consider random i.i.d. samples of absolutely continuous measures on bounded connected d...
We consider random i.i.d. samples of absolutely continuous measures on bounded connected domains. We...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
Abstract. Let µN be the empirical measure associated to a N-sample of a given probability distributi...
The Wasserstein distance between two probability measures on a metric spaceis a measure of closeness...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
An upper bound is given for the mean square Wasserstein distance between the empirical measure of a ...
We provide some non-asymptotic bounds, with explicit constants, that measure the rate of convergence...
We propose a “decomposition method” to prove non-asymptotic bound for the convergence of empirical m...
We consider a class of partial mass problems in which a fraction of the mass of a probability measur...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
In this article, we define the transport dimension of probability measures on $\mathbb{R}^m...
This paper is focused on the statistical analysis of probability measures $\bnu_{1},\ldots,\bnu_{n}$...
We consider a situation where two sample sets of independent real valued observations are obtained f...
Abstract. This note reviews, compares and contrasts three notions of “dis-tance ” or “size ” that ar...
Abstract. We consider random i.i.d. samples of absolutely continuous measures on bounded connected d...
We consider random i.i.d. samples of absolutely continuous measures on bounded connected domains. We...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
Abstract. Let µN be the empirical measure associated to a N-sample of a given probability distributi...
The Wasserstein distance between two probability measures on a metric spaceis a measure of closeness...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
An upper bound is given for the mean square Wasserstein distance between the empirical measure of a ...
We provide some non-asymptotic bounds, with explicit constants, that measure the rate of convergence...
We propose a “decomposition method” to prove non-asymptotic bound for the convergence of empirical m...
We consider a class of partial mass problems in which a fraction of the mass of a probability measur...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
In this article, we define the transport dimension of probability measures on $\mathbb{R}^m...
This paper is focused on the statistical analysis of probability measures $\bnu_{1},\ldots,\bnu_{n}$...
We consider a situation where two sample sets of independent real valued observations are obtained f...
Abstract. This note reviews, compares and contrasts three notions of “dis-tance ” or “size ” that ar...
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