Timeline for What location parameter is modelled by robust regression?
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| Jun 11, 2020 at 14:32 | history | edited | CommunityBot |
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| Mar 20, 2017 at 4:59 | comment | added | Glen_b | Thanks @kjetil -- I have replaced the link with the place it moved to. | |
| Mar 19, 2017 at 16:30 | comment | added | kjetil b halvorsen♦ | @Glen_b: The document Robust.pdf you referenced (by Brian Ripley) seems to have disappeared from the web, unfortunately ... | |
| Aug 29, 2014 at 22:10 | comment | added | Frank Harrell | I prefer to estimate quantities that are at least somewhat easy to understand: means, mean absolute differences (for variability), quantiles, exceedance probabilities. Cumulative probability ordinal models are robust and can estimate a wide variety of quantities successfully. They do not allow "outliers" on $Y$ to affect regression coefficients, but outliers will have an impact when using these models to estimate the mean. | |
| Aug 29, 2014 at 21:49 | history | edited | Glen_b | CC BY-SA 3.0 |
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| Aug 29, 2014 at 16:30 | comment | added | Nick Cox | Indeed, but what is strange is a matter of sociology also. In principle we could use more advanced summaries for univariate work; in practice the more advanced methods are applied more to model fitting for responses given predictors, i.e. robust regression is what everyone in the field most wants to devise (or use). Trimming methods are apparently widely used in some sports and competitive activities in which top and bottom judges' ratings are discounted to discourage or downweight partisan biases. So, some subtle methods are widely used outside "science" as usually defined. | |
| Aug 29, 2014 at 16:15 | comment | added | Michael M | @NickCox: Quite similar thoughts led me to ask the question. Isn't it a bit strange then to model a quantity that is hardly ever used in univariate statistics? | |
| Aug 29, 2014 at 15:02 | comment | added | Glen_b | @Peter The book by Staudte and Sheather is a bit more recent and may be suitable for some purposes. This document by Brian Ripley isn't too bad if you want a short introduction. | |
| Aug 29, 2014 at 14:35 | comment | added | Glen_b | @Peter Best kind of depends on a lot of things - what sort of treatment one seeks, for example. I also prefer to read many sources rather than one - including papers, talks, notes as well as books. But the classic references of Huber (1981) and Hampel et al (1986) are still pretty relevant (see the wikipedia page on Robust statistics for complete references and a number of other good ones). | |
| Aug 29, 2014 at 11:58 | comment | added | Nick Cox | I would say that the estimand is whatever the estimator estimates, so the question is backward in one sense. On a simple analogue: you are at liberty to regard the sample median as a robust estimator of the mean, and that may be your motivation for using it, but it is as simple or simpler to regard the sample median as an estimator of the population median. More discussion, including other points of view, at stats.stackexchange.com/questions/63386/… | |
| Aug 29, 2014 at 10:46 | comment | added | Peter Flom | An excellent answer as usual @Glen_b . What's your idea of the best reference for this sort of thing? | |
| Aug 29, 2014 at 9:00 | history | edited | Glen_b | CC BY-SA 3.0 |
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| Aug 29, 2014 at 8:54 | history | answered | Glen_b | CC BY-SA 3.0 |