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pith:2026:AJ3XOPGRAMU5BJBYQO344H4IOZ
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Nearest-Neighbour Matching on Unbounded Supports and Covariate Shift Transfer

Simon Viel

Nearest-neighbour matching achieves usual convergence rates on unbounded supports via a transferability measure between source and target distributions.

arxiv:2605.16027 v1 · 2026-05-15 · math.ST · stat.TH

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Claims

C1strongest claim

We show that the usual rates of convergence can be achieved with minimal assumptions on the covariate supports. These assumptions are replaced with conditions on the source and target distributions, among which a measure of the transferability between the two probability measures.

C2weakest assumption

A finite transferability measure exists between the source and target probability measures that is sufficient to control the bias and variance terms arising in the nearest-neighbour estimator.

C3one line summary

Nearest-neighbour matching achieves usual convergence rates under general transferability conditions on source-target distribution pairs, relaxing compact support and bounded density assumptions.

References

33 extracted · 33 resolved · 0 Pith anchors

[1] Convergence rate for Nearest Neighbour matching: geometry of the domain and higher-order regularity , author=. 2025 , journal= 2025
[2] The Annals of Statistics , volume= 2026
[3] Journal of Machine Learning Research , volume=
[4] Large sample properties of matching estimators for average treatment effects , author=. Econometrica , volume=
[5] On the failure of the bootstrap for matching estimators , author=. Econometrica , volume=

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First computed 2026-05-20T00:01:49.856403Z
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Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

0277773cd10329d0a43883b7ce1f887676533ded33d9a29ec281d746fd4fa30c

Aliases

arxiv: 2605.16027 · arxiv_version: 2605.16027v1 · doi: 10.48550/arxiv.2605.16027 · pith_short_12: AJ3XOPGRAMU5 · pith_short_16: AJ3XOPGRAMU5BJBY · pith_short_8: AJ3XOPGR
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/AJ3XOPGRAMU5BJBYQO344H4IOZ \
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Canonical record JSON
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