There are also robust estimators in econometrics; then it means something about good behaviour under heteroskedastic and/or correlated error. Robust Bayes means something about inference that is robust to the choice of prior (which could overlap but is a rather different emphasis).
Outlier robustness is AFAICT more-or-less a frequentist project. Bayesian approaches seem to achieve robustness largely by choosing heavy-tailed priors or heavy-tailed noise distributions where they might have chosen light-tailed ones, e.g. Laplacian distributions instead of Gaussian ones. Such heavy-tailed distributions may have arbitrary prior parameters, but not more arbitrary than usual in Bayesian statistics and therefore do not attract so much need to wash away the guilt as frequentists seem to feel.
One can off course use heavy-tailed noise distributions in frequentist inference as well and that will buy a kind of robustness. That seems to be unpopular due to making frequentist inference as difficult as Bayesian inference.
- Random (mixture) corruption
- (Adversarial) total variation \(\epsilon\)-corruption.
- wasserstein corruption models (does one usually assume adversarial here or random) as seen in “distributionally robust” models.
M-estimation with robust loss
The one that I, at least, would think of when considering robust estimation.
In M-estimation, instead of hunting an maximum of the likelihood function as you do in maximum likelihood, or a minimum of the sum of squared residuals, as you do in least-squares estimation, you minimise a specifically chosen loss function for those residuals. You may select an objective function more robust to deviations between your model and reality. Credited to Huber (1964).
See M-estimation for some details.
AFAICT, the definition of M-estimation includes the possibility that you could in principle select a less-robust loss function than least sum-of-squares or negative log likelihood, but I have not seen this in the literature. Generally, some robustified approach is presumed.
For M-estimation as robust estimation, various complications ensue, such as the different between noise in your predictors, noise in your regressors, and whether the “true” model is included in your class, and which of these difficulties you have resolved or not.
Loosely speaking, no, you haven’t solved problems of noise in your predictors, only the problem of noise in your responses.
And the cost is that you now have a loss function with some extra arbitrary parameters in which you have to justify, which is anathema to frequentists, who like to claim to be less arbitrary than Bayesians. You then have to justify why you chose that loss function and its particular parameterisation. There are various procedures to choose these parameters, however.
🏗 Don’t know
Rousseeuw and Yohai’s idea. (P. Rousseeuw and Yohai 1984)
Many permutations on the theme here, but it rapidly gets complex. The only one of these families I have looked into are the near trivial cases of the Least Median Of Squares and Least Trimmed Squares estimations. (P. J. Rousseeuw 1984) ] More broadly we should also consider S-estimators, which do something with… robust estimation of scale and using this to do robust estimation of location? 🏗
Theil-Sen-(Oja) estimators: Something about medians of inferred regression slopes. 🏗
Tukey median, and why no-one uses it what with it being NP-Hard.
RANSAC — some kind of randomised outlier detection estimator. 🏗
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