Linear Response Estimators for Singular Statistical Models
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Abstract (arXiv)
We define susceptibilities as a measure of the response of an observable quantity of a parameterized statistical model to a perturbation of the data for a general class of observables. We define estimators for these susceptibilities as statistics in a sequence of n data-points and prove that these estimators are consistent and asymptotically unbiased in the large n regime.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 8 May 2026.
Builds on
7 of 23 references.
References · 7
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
- 2017Covariances, Robustness, and Variational Bayescited by 39
- 2024Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficientcited by 39
- 2025Bayesian Influence Functions for Hessian-Free Data Attributioncited by 12
- 2025Structural Inference: Interpreting Small Language Models with Susceptibilitiescited by 11
- 2026Patterning: The Dual of Interpretabilitycited by 6
- 2025From Global to Local: A Scalable Benchmark for Local Posterior Samplingcited by 5
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Citing papers per year: 2026: 1.
1 paper.