Hi everyone, I’m new to Julia and have been trying it out for the past few weeks.
As I’m still only beginning to (re)learn statistics as well, I’ve set out to get some insight into the following problem (I didn’t want to start with neural nets although if that is the answer to my question i will pursue that avenue)
I was wondering on how to model a clothing-size distribution for a shop having to buy a multitude of clothing items, these items have categorical traits like brand, type and color, but, they have two sizes (i.e. length and width). I’ve been making contour plots of the frequencies these sizes occur, which already provided some insight. (ie: brandX doesn’t have the same 2d-histogram as brandY).
I’ve tried different approaches: a sampler using two Categoricals (from Distributions.jl) and I also tried fitting an MvNormal (although the sizes are of course discrete)
In order to fill an inventory of items I would then use said distribution and sample N sizes out of them. But the fact that some items will have low kardinality is something I’d like to resolve. I’ve been reading about Mixed-effect models, but I’m not sure I’m on the right track.
More so, there are some gaps in my statistical knowledge, maybe I’m missing something obvious.
I was thinking that a different coloured item might have somewhat of the same size distribution, but how would you quantify this? I keep running into walls because of the 2-dim of the problem as well.
Furthermore: items from the same brand or type might also have size-distribution similarity. How would a model account for that?
As a naive approach I was thinking a kmeans-index and use that to sample data from for the more sparse items. But I’m unsure this i a good direction to be taken.
Anything I can read or study? Pointers are more than welcome.