Cited by 499Advanced Statistical Methods and ModelsStatistical Methods and Bayesian InferenceStatistical and numerical algorithms

On The So-Called “Huber Sandwich Estimator” and “Robust Standard Errors”

Freedman, David A·2006·The American Statistician

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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Crossref · 10.1198/000313006x152207

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  • Using robust (White) standard errors to compute 90% Wald confidence intervals ensures valid inference even when certain model assumptions are violated.

    “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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Authors
Freedman, D. A.
Affiliations
University of California, Berkeley (US)
Corresponding author
Freedman, David A
Venue
The American Statistician
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

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