Timeline for How often should a statistical model (lets say logistic regression) be evaluated?
Current License: CC BY-SA 4.0
7 events
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| Apr 21, 2022 at 14:34 | comment | added | gung - Reinstate Monica | @runr, you can always ask a new question, if you want. It strikes me as a much bigger / more complicated topic than would be able to be covered in an answer here. Maybe someone would be able to point you towards a book or some papers, IDK. | |
| Apr 21, 2022 at 13:53 | comment | added | runr | @gung-ReinstateMonica Thanks for your response. In your opinion, Is the 3rd point of your comment worth asking a new question, or are such approaches well known and answered/defined in the literature/SE? This should be a common problem in practice, however, I've rarely seen any literature on addressing it except for "reject inference" in some very specific (accept/reject client) contexts. | |
| Apr 20, 2022 at 17:11 | comment | added | gung - Reinstate Monica | @runr, I'm not primarily a Bayesian (although, I'm not dogmatically opposed, either). There are several things that are involved in your question. 1) Bayesian updating needn't be involved in 'significance', just refining the posterior distribution. 2) Missing data & causal inferences can be addressed in a Bayesian setting not unlike in a Frequentist setting. 3) If you're using the model to make decisions that impact future data, that whole system should be modelable in a sufficiently complex setup. Others may be able to provide more detailed answers. | |
| Apr 20, 2022 at 17:01 | comment | added | runr | @gung-ReinstateMonica Does this also work if (such) model is affecting the generation of the new data (that will be used for future updates)? Assuming OP, based on the predictions of the current/previous models, rejects certain client applications and thus is not collecting data of a certain part of the population, how will future model updates be affected? Likely, such continuous updates can erroneously discard some variables as insignificant (e.g., large debt), since every new data sample will be from clients without debt. Can one continue updating old models without generating such bias? | |
| Apr 20, 2022 at 16:18 | comment | added | gung - Reinstate Monica | If someone wants to continually update their model, I would think they should just switch to a Bayesian analysis in which their initial model is the prior, and they can update with new data as often as they like. | |
| Apr 20, 2022 at 16:10 | history | edited | Frank Harrell | CC BY-SA 4.0 |
fixed broken link
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| Sep 5, 2017 at 13:16 | history | answered | Frank Harrell | CC BY-SA 3.0 |