I am trying to build a (very simple) recommender system on which statistical model to use, as a learning exercise. It only targets inference (not prediction).
Doing that, it would be nice to isolate a set of best-fitting distributions for the chosen variable, as a part of model assumption verification. Reading on various Q&As on this website [1, 2, 3] and R packages, I understand this to be a most difficult issue.
From answers to other questions, I do understand that no single particular distribution can be isolated, but it seems to me that a set of best-fitting distributions can be identified. Am I mistaken?
For example, a (very naive) way would be to sort the kernel density function of the tested variable against a coefficient cutoff obtained through comparison of the targeted distributions kernel density functions, using some kind of pattern recognition algorithm...
And what about the fitdistrplus R package?
Can this be done?
