NutchaW / covid19_forecast_similarity

A repository for forecast similarity analysis
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comments to address in preprint #20

Open nickreich opened 8 months ago

nickreich commented 8 months ago

Comments from Johannes

nickreich commented 8 months ago

Addressing the comment about "Permutations were restricted to be on the model types only and the permuted model types were kept constant across locations and target end dates."

My sense is that another way of saying this is that to create one permuted dataset, we did NOT just permute the entire column of model types across all locations, dates, etc... but rather we took the a table of models (one row per model with columns for model name and model type) and permuted the model type column. Then this was joined back in to the large dataset so that all rows pertaining to a given model were assigned that model's permuted model-type value. Would like some validation on this interpretation before trying to clarify in the main text.

nickreich commented 8 months ago

Making a note regarding question above about notation on p5. I worked through this in detail (see attached whiteboard shot) and think it all checks out. (Although I didn't cross-check the approximation sum against what is coded up.) Yes it is complicated (perhaps overly so), but it does seem to check out. Regarding the "counting q's twice" I didn't see that as a problem in my working out of it, as I think the {q^G-q^F} takes care of that. I think Johannes' suggestion about the max also would work if you defined \tau_0 = 0 and looked at values of j between 0 and 2*K-1. That might be simpler but I guess I don't see a huge value in changing it, as it doesn't change much of the overall complexity of the set-up and I'm not convinced that it's wrong as it stands now.

PXL_20231221_212511620