Timeline for Rank versus Box-Cox transformation
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 12, 2016 at 5:06 | comment | added | kjetil b halvorsen♦ | the box-cox transform parameter as usually estimated (by maximizing a likelihood) is more trying to stabilize variance that to normalize residuals! And frankly, constant variance is a much more important assumption than normality. More of a concern with box-cox is that the transform changes the meaning of the parameters in a linear model! so, if that is a concern (it should be), then some glm might be better. | |
| May 14, 2015 at 18:38 | vote | accept | dfife | ||
| May 14, 2015 at 16:11 | comment | added | Anthony | I agree that ranks pose problems for out of sample prediction, although rank transformed regression isn't as horrible as one might expect it to be on the face of it (Iman & Conover, 1979). I am interpreting @dfife's question, which focuses on nonparametric tests and p-values, as an interest is in establishing the existence of a monotonic relationship rather than applying the regression coefficients to other datasets. For this specific goal, I am suggesting that the limitations of rank transformation may sometimes be outweighed by its benefits. | |
| May 14, 2015 at 15:49 | comment | added | whuber♦ | I don't quite understand the comparisons you are making because the "rank transformation" is not a re-expression of the values in the way a Box-Cox transformation is. The difference is that the ranks depend on the dataset and are undefined for any other values, whereas the Box-Cox transformation (although estimated from the data) does not depend on the dataset and can be applied to any other values. This makes it difficult to see how ranks could be of use in problems of regression (as opposed to correlation or association). This question appears to be from a regression perspective. | |
| May 14, 2015 at 15:31 | history | answered | Anthony | CC BY-SA 3.0 |