Timeline for alternative to IQR [duplicate]
Current License: CC BY-SA 4.0
23 events
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| Jan 4, 2019 at 14:27 | history | closed | gung - Reinstate Monica | Duplicate of Is there a boxplot variant for Poisson distributed data? | |
| Jan 4, 2019 at 14:17 | history | edited | Nick Cox | CC BY-SA 4.0 |
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| Jan 4, 2019 at 13:59 | history | edited | kjetil b halvorsen♦ |
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| Apr 1, 2018 at 15:15 | history | edited | kjetil b halvorsen♦ |
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| Mar 1, 2016 at 19:54 | comment | added | Nick Cox | As I understand it you are proposing this as an estimator of the SD and the rule is calculate the SD as usual, but to ignore points outside your limits. So, it seems that this estimator is necessarily biased and the question is then how much and does it matter. I'd expect simulation to more illuminating here than any other mode of argument. My own preference, FW little IW, is to use something other than the SD whenever I doubt the SD. I think you'd have a hard job convincing people that this was easier to think about and less arbitrary than IQR or MAD. | |
| Mar 1, 2016 at 16:13 | comment | added | Nenunathel | That is exactly what I was looking for. However it would be nice to still have somebody mention whether my proposed method is useful and at what point I should start using other methods such as M-estimators. | |
| Mar 1, 2016 at 14:35 | comment | added | user603 | @user3604362: Thank you for the additional clarification. Assuming I understood what you want (an univariate outlier detection method with 25% breakdown that takes the assymetry of the center of the data into account?) Maybe have a look at the adjusted boxplot. | |
| Mar 1, 2016 at 14:15 | comment | added | dsaxton | All summary statistics by their very nature discard some information from the sample, so you will always find such limitations. If you want to know something about the skewness then do not look at the IQR, look at the skewness. The IQR is intended as a robust measure of dispersion and is a fairly concise way of summarizing this information. | |
| Mar 1, 2016 at 14:14 | comment | added | Nenunathel | I have added an image to what I meant. Maybe that helps? | |
| Mar 1, 2016 at 14:14 | history | edited | Nenunathel | CC BY-SA 3.0 |
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| Mar 1, 2016 at 13:41 | comment | added | user603 | I really have a hard time understanding your second paragraph. In particular, can you try to reformulate the second sentence in that paragraph? | |
| Mar 1, 2016 at 13:26 | history | edited | Nenunathel | CC BY-SA 3.0 |
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| Mar 1, 2016 at 13:22 | comment | added | Nenunathel | Right, well, assuming I get a distribution similar to what is shown above, is this method recommendable? And what other method might be better instead? | |
| Mar 1, 2016 at 11:51 | comment | added | Nick Cox | It is quite fallacious to suppose that symmetry of the quartiles around the median requires normality. That would be true e.g. of logistic and t distributions. You could even construct distributions with quartiles equally distant from the median but asymmetric overall. | |
| Mar 1, 2016 at 11:50 | comment | added | Nick Cox | The IQR is just what it is defined to be; there is no assumption of symmetry; equally nothing means that it can be interpreted easily without looking at median and quartiles. You might as well say that the SD assumes symmetry around the mean; not so, and nothing stops that being useful for distributions that are right-skewed, e.g. exponentials and Poissons. | |
| Mar 1, 2016 at 11:28 | comment | added | Nenunathel | Silverfish, through the box and whiskers plot, you can see how the median is closer to one quartile than the other, thus showing that the data is skewed. | |
| Mar 1, 2016 at 11:26 | history | edited | Nenunathel | CC BY-SA 3.0 |
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| Mar 1, 2016 at 11:21 | history | edited | Silverfish | CC BY-SA 3.0 |
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| Mar 1, 2016 at 11:19 | comment | added | Silverfish | "the IQR having at least the advantage of showing the skewedness through the box and whiskers plot" - I'm not sure I follow this. On its own, the IQR is only a measure of dispersion, not of skewness. | |
| Mar 1, 2016 at 11:18 | comment | added | Nenunathel | Hm, that is interesting! I was never shown this. However, it only considers the shorter side to make a range around the median, whereas in my case, I use the shorth to make a range on that side, and the other side I use a longer distance. That way it follows better the nature of the data. | |
| Mar 1, 2016 at 11:13 | review | First posts | |||
| Mar 1, 2016 at 11:19 | |||||
| Mar 1, 2016 at 11:09 | comment | added | Tim | Check this answer about shorth: stats.stackexchange.com/questions/76848/… | |
| Mar 1, 2016 at 11:06 | history | asked | Nenunathel | CC BY-SA 3.0 |