Talk:Robust statistics

I think this article could be improved greatly if it would provide some information about how robust statistics could be used in practice. I think that's the real goal of this field is to use it to improve estimates. For example, show how you would use robust stastics to improve the linear regression of a sequence of linear measurements with a single outlier (as the problem is demonstrated in the regression analysis article). This article should demonstrate a solution!

Breakdown estimator

The article has a discrepancy between the breakdown point - it states it is 1/N in one section, and 0 in another. Which is it?

Book review

This part doesnt seem wikipediaish:

Good books on robust statistics include those by Huber (1981), Hampel et al (1986) and Rousseeuw and Leroy (1987). A modern treatment is given by Maronna et al (2006). Huber's book is quite theoretical, whereas the book by Rousseew and Leroy is very practical (although the sections discussing software are rather out of date, the bulk of the book is still very relevant). Hampel et al (1987) and Maronna et al (2006) fall somewhere in the middle ground. All four of these are recommended reading, though Maronna et al is the most up to date. —Preceding unsigned comment added by 192.38.121.18 (talk) 09:06, 15 April 2009 (UTC)

"classical statistical methods"

"classical statistical methods" is not defined. does this refer to parametric statistics? if so, a link in the summary would be appropriate. —Preceding unsigned comment added by Landroni (talk • contribs) 21:22, 21 May 2009 (UTC)

Info-Gap decision theory

It should be pointed out that info-gap's robustness model is a simple instance of Wald's famous Maximin model. The text should be modified accordingly. Sniedo (talk) 10:12, 30 June 2009 (UTC)

Empirical influence function

The section is just absurdly technical for a general reference work. —Preceding unsigned comment added by 150.203.23.163 (talk) 05:42, 15 June 2010 (UTC)

I can only agree, so I've added a {{technical|section=…}} tag to it. --Qwfp (talk) 09:15, 15 June 2010 (UTC)

Can't discern meaning

The introduction has the sentence "Unfortunately, when there are outliers in the data, classical methods often have very poor performance, like standard Kalman filters, which are not robust to them". I can't work out what the emboldened part means, in particular what "them" is. Could someone knowledgeable, either rewrite that sentence or (probably better) put it into two sentences, please? :-) 78.147.61.105 (talk) 16:56, 10 April 2012 (UTC)

I think the "them" was the outliers. I have rewritten the lead and intro sections to try to clarify things, including the "classical methods" complained of above. Melcombe (talk) 22:24, 16 April 2012 (UTC)

RANSAC

My best practical tool in robust statistics is the algorithm RANSAC. It is used heavily in computer vision, where mostly the data or measurements are of two types : a) conforming to the model, and b) not conforming to the model (outliers or noise). And the data conforming to the model is often just affected by smaller gaussian (normally distributed) errors. Sees this all the time in my applications in computer vision where the interpretation of some of the phenomena seen in the image stream often need to be seen like the two types mentioned. The main drawback is that it is a slow algorithm, so use only on vital data (in realtime applications).— Preceding unsigned comment added by 2001:4643:E6E3:0:5530:EF4D:105D:7523 (talk) 09:30, 1 May 2018 (UTC)

Change slightly?

I am confused by "The median is a robust measure of central tendency. Taking the same dataset {2,3,5,6,9}, if we add another datapoint with value -1000 or +1000 then the median will change slightly" I think it would not change at all? — Preceding unsigned comment added by 60.168.149.14 (talk) 08:45, 25 February 2019 (UTC)