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Coming from the field of computer vision, I've often used the RANSAC (Random Sample Consensus) method for fitting models to data with lots of outliers.

However, I've never seen it used by statisticians, and I've always been under the impression that it wasn't considered a "statistically-sound" method. Why is that so? It is random in nature, which makes it harder to analyze, but so are bootstrapping methods.

Or is simply a case of academic silos not talking to one another?

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I wonder one thing about computer vision methods vs. statistics methods: performance in the first is a must. Maybe there's a trade-off between performance and "correctness", and computer vision and statistics have different weights for those variables. – Lucas Reis Jul 31 '12 at 15:26

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For us, it is just one example of a robust regression -- I believe it is used by statisticians also, but maybe not so wide because it has some better known alternatives.

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Can you give examples of alternatives? I'd like to look into that. – Bossykena Jul 21 '10 at 20:56
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The mostly known and the simplest is the Median-Median regression, well known from smart calculators (Sigh!). Consult also Wikipedia en.wikipedia.org/wiki/Robust_regression and maybe CRAN's Robust task view cran.r-project.org/web/views/Robust.html – mbq♦ Jul 21 '10 at 21:20
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This sounds a lot like bagging which is a frequently used technique.

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