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On The So-Called “Huber Sandwich Estimator” and “Robust Standard Errors”
Abstract
The “Huber Sandwich Estimator” can be used to estimate the variance of the MLE when the underlying model is incorrect. If the model is nearly correct, so are the usual standard errors, and robustification is unlikely to help much. On the other hand, if the model is seriously in error, the sandwich may help on the variance side, but the parameters being estimated by the MLE are likely to be meaningless—except perhaps as descriptive statistics.
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“This type of standard errors ensures valid inference even when certain model assumptions are violated.”
From Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge (2025) · cites this paper as Freedman, 2006
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Metadata
- Authors
- Freedman, D. A.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Freedman, David A
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2006-11
- Volume
- 60
- Issue
- 4
- Pages
- 299-302
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2018502278
- Cited by
- 498
- References
- 19
- Citations by year
- 2026: 33 · 2025: 34 · 2024: 40 · 2023: 41 · 2022: 27 · 2021: 43 · 2020: 34 · 2019: 20 · 2018: 49 · 2017: 17 · 2016: 23 · 2015: 31 · 2014: 27 · 2013: 19 · 2012: 17
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The American Statistician (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- robust standard errors, model misspecification, maximum likelihood estimation, variance estimation, heteroscedasticity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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