Wasserstein-Cramér-Rao Theory of Unbiased Estimation

• URL: http://arxiv.org/abs/2511.07414v1
• Date: Mon, 10 Nov 2025 18:58:18 GMT
• Title: Wasserstein-Cramér-Rao Theory of Unbiased Estimation
• Authors: Nicolás García Trillos, Adam Quinn Jaffe, Bodhisattva Sen,
• Abstract summary: We are interested in a quantity which represents the instability of an estimator when its value is compared to the value for an independently-sampled data set.<n>The resulting theory of sensitivity is based on the Wasserstein geometry in the same way that the classical theory of variance is based on the Fisher-Rao geometry.
• Score: 7.111443975103331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quantity of interest in the classical Cram\'er-Rao theory of unbiased estimation (e.g., the Cram\'er-Rao lower bound, its exact attainment for exponential families, and asymptotic efficiency of maximum likelihood estimation) is the variance, which represents the instability of an estimator when its value is compared to the value for an independently-sampled data set from the same distribution. In this paper we are interested in a quantity which represents the instability of an estimator when its value is compared to the value for an infinitesimal additive perturbation of the original data set; we refer to this as the "sensitivity" of an estimator. The resulting theory of sensitivity is based on the Wasserstein geometry in the same way that the classical theory of variance is based on the Fisher-Rao (equivalently, Hellinger) geometry, and this insight allows us to determine a collection of results which are analogous to the classical case: a Wasserstein-Cram\'er-Rao lower bound for the sensitivity of any unbiased estimator, a characterization of models in which there exist unbiased estimators achieving the lower bound exactly, and some concrete results that show that the Wasserstein projection estimator achieves the lower bound asymptotically. We use these results to treat many statistical examples, sometimes revealing new optimality properties for existing estimators and other times revealing entirely new estimators.

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