# Toward an Asymptotic Efficiency Theory on Regular Parameter Manifolds

arXiv: https://arxiv.org/abs/2510.13703 · HTML: https://mlsanity.com/paper/2510.13703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 118 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [Fr'echet Regression for Random Objects with Euclidean Predictors](https://mlsanity.com/paper/1608.03012/) (2016)
- [Wasserstein Regression](https://mlsanity.com/paper/2006.09660/) (2020)
- [Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond](https://mlsanity.com/paper/1912.12945/) (2019)
- [Random Forest Weighted Local Fr'echet Regression with Random Objects](https://mlsanity.com/paper/2202.04912/) (2022)
- [Causal Inference on Distribution Functions](https://mlsanity.com/paper/2101.01599/) (2021)
- [One-Step Estimation of Differentiable Hilbert-Valued Parameters](https://mlsanity.com/paper/2303.16711/) (2023)
- [Simplifying debiased inference via automatic differentiation and probabilistic programming](https://mlsanity.com/paper/2405.08675/) (2024)
- [Higher Order Tangent Spaces and Influence Functions](https://mlsanity.com/paper/1502.00812/) (2015)
- [Wasserstein F-tests and Confidence Bands for the Fr`echet Regression of Density Response Curves](https://mlsanity.com/paper/1910.13418/) (2019)
- [Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference](https://mlsanity.com/paper/2508.17099/) (2025)
- [Total Variation Regularized Fr'echet Regression for Metric-Space Valued Data](https://mlsanity.com/paper/1904.09647/) (2019)
- [Intrinsic Riemannian Functional Data Analysis for Sparse Longitudinal Observations](https://mlsanity.com/paper/2009.07427/) (2020)
- [Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data](https://mlsanity.com/paper/2409.09973/) (2024)
- [Quantiles and Quantile Regression on Riemannian Manifolds: a measure-transportation-based approach](https://mlsanity.com/paper/2410.15711/) (2024)
- [Efficient Estimation in Single Index Models through Smoothing splines](https://mlsanity.com/paper/1612.00068/) (2016)
- [Uncertainty Quantification of Spectral Estimator and MLE for Orthogonal Group Synchronization](https://mlsanity.com/paper/2408.05944/) (2024)
- [High-order Accurate Inference on Manifolds](https://mlsanity.com/paper/2501.06652/) (2025)
- [Nuisance parameters and elliptically symmetric distributions: a geometric approach to parametric and semiparametric efficiency](https://mlsanity.com/paper/2506.23213/) (2025)

## Abstract (arXiv)

Asymptotic efficiency theory is one of the pillars in the foundations of modern mathematical statistics. Not only does it serve as a rigorous theoretical benchmark for evaluating statistical methods, but it also sheds light on how to develop and unify novel statistical procedures. For example, the calculus of influence functions has led to many important statistical breakthroughs in the past decades. Responding to the pressing challenge of analyzing increasingly complex datasets, particularly those with non-Euclidean/nonlinear structures, many novel statistical models and methods have been proposed in recent years. However, the existing efficiency theory is not always readily applicable to these cases, as the theory was developed, for the most part, under the often neglected premise that both the sample space and the parameter space are normed linear spaces. As a consequence, efficiency results outside normed linear spaces are quite rare and isolated, obtained on a case-by-case basis. This paper aims to develop a more unified asymptotic efficiency theory, allowing the sample space, the parameter space, or both to be Riemannian manifolds satisfying certain regularity conditions. We build a vocabulary that helps translate essential concepts in efficiency theory from normed linear spaces to Riemannian manifolds, such as (locally) regular estimators, differentiable functionals, etc. Efficiency bounds are established under conditions parallel to those for normed linear spaces. We also demonstrate the conceptual advantage of the new framework by applying it to two concrete examples in statistics: the population Frechet mean and the regression coefficient vector of Single-Index Models.