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Jul 9 at 9:00 history protected kjetil b halvorsen
Jul 9 at 6:39 answer added Gene Uniana timeline score: 1
Feb 5 at 12:50 history edited kjetil b halvorsen
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Jan 4, 2019 at 6:35 vote accept Andre
Oct 30, 2017 at 2:49 vote accept Andre
Jan 4, 2019 at 6:35
Oct 28, 2017 at 6:16 vote accept Andre
Oct 28, 2017 at 6:16
Oct 28, 2017 at 6:16 vote accept Andre
Oct 28, 2017 at 6:16
Oct 27, 2017 at 19:32 comment added Andre @whuber or ask questions on Cross Validated :)
Oct 27, 2017 at 19:30 answer added Andre timeline score: 1
Oct 27, 2017 at 16:46 comment added whuber @Matthew It's a losing battle: new books on some version of R plus statistics are appearing faster than any one person could even read them. A better response is to write a better book--but that's obviously a significant effort!
Oct 27, 2017 at 16:01 comment added Matthew Drury @whuber That "since then" is particularly bad. Have you ever considered writing the authors of these texts?
Oct 27, 2017 at 15:41 answer added Aksakal timeline score: 3
Oct 26, 2017 at 16:31 comment added kjetil b halvorsen @whuber: thanks for this references, I will chech them before extending my answer.
Oct 26, 2017 at 16:16 comment added Andre @whuber okay, I see! Thanks so much for clarification.
Oct 26, 2017 at 16:16 comment added whuber I'm not making this up, either: see John Tukey's book EDA (Addison-Wesley 1977) or Hoaglin, Mosteller, & Tukey, Understanding Robust and Exploratory Data Analysis (J. Wiley 1983).
Oct 26, 2017 at 16:13 comment added whuber Let me be perfectly clear: the purpose of the Box-Cox transformation is not to make data look as normal as possible, nor is it necessary to calculate it with that aim in mind. Moreover, the aims of (1) achieving a symmetric (or Normal) distribution of residuals, (2) linearizing a relationship, and (3) achieving constant conditional variance are distinctly different and will not necessarily be achieved by the same (or any) Box-Cox transformation. The quotation is a narrow, specialized approach to finding a Box-Cox transformation and its inference ("since then...") is flatly incorrect.
Oct 26, 2017 at 15:38 comment added Andre @whuber I agree that box-cox method has so many uses, but what I’m also confused is in linear regression we don’t assume x and y to be normally distributed while what box-cox transformation calculates is actually to make them as normal as possible. And the quotation explains that this is because then we are more confident that x and y have linear relationship. But if so, why don’t we assume this for linear regression?
Oct 26, 2017 at 15:22 history tweeted twitter.com/StackStats/status/923570489192271873
Oct 26, 2017 at 14:40 comment added whuber Neither the quotation nor the preceding comment are fully general. Although the Box-Cox transformation can be implemented with the aims given in the quotation and using an ML method alluded to by @Kjetil, yet it's far more general than that: (1) it can be used to symmetrize distributions and/or create near-constant variances and/or linearize relationships; (2) it should be estimated using robust exploratory methods rather than the much more limited parametric maximum likelihood methods offered in most software packages. See stats.stackexchange.com/a/3530, for instance.
Oct 26, 2017 at 11:00 answer added kjetil b halvorsen timeline score: 6
Oct 26, 2017 at 6:28 comment added kjetil b halvorsen In reality, box-cox transformation finds a transformation that homogenizes variance, and constant variance is an assumption! The crux of the matter is that boxcox uses a constant-variance normal likelihood.
Oct 26, 2017 at 5:50 history asked Andre CC BY-SA 3.0