Timeline for Does parametrized Box Cox transform take degrees of freedom away from subsequent models?
Current License: CC BY-SA 4.0
7 events
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| Feb 3, 2022 at 18:11 | comment | added | whuber♦ | The short answer, based on maximum likelihood theory, is that each Box-Cox parameter eats one D.F. For an explicit discussion of this in the context of logistic regression, a classic paper is Royston & Altman, Regression Using Fractional Polynomials of Continuous Covariates... Appl. Stat. (1994) 43 ,No.3, pp.429-467. Find it in pdf form at rss.onlinelibrary.wiley.com/doi/pdf/10.2307/2986270 . | |
| Feb 3, 2022 at 17:53 | comment | added | Galen | @whuber I was hoping that by taking a descriptivist approach rather than a prescriptivist approach that I might learn something new about degrees of freedom through this question. I have a bulding worry that DF are just ad hoc scores. We already have a lot of "what are degrees of freedom?" questions on stats.SE, so I was aiming for something more subtle. Your questions exactly reflect my concern. I guess I achieved writing an obscure question instead of a subtle one. | |
| Dec 24, 2021 at 16:50 | comment | added | whuber♦ | It depends on what you mean by "degrees of freedom" and how you intend to use this quantity in follow-on calculations. Could you explain? | |
| Dec 24, 2021 at 7:03 | history | edited | Galen | CC BY-SA 4.0 |
Added code around what is equivalently the dot notation in Python to the function.
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| Dec 24, 2021 at 6:55 | answer | added | Demetri Pananos | timeline score: -1 | |
| Dec 24, 2021 at 6:55 | comment | added | Galen | Related: stats.stackexchange.com/questions/40779/… | |
| Dec 24, 2021 at 6:51 | history | asked | Galen | CC BY-SA 4.0 |