In the course of pursuing faster null fitting for categorical covariates, it occurred to me that we may be able to speed up fitting under the alternative using a trivial technique:
In any discrete X setting with p categories I think we can immediately collapse Y from n x J to p x J, summing over the same X's. This may reduce computation especially for large n. This should work for any g().
I can't remember if scaling in n with radEmu is poor, but this could be worth a try. There's no need to keep rows as distinct, as any time they appear in the likelihood there is going to be aggregation over common X's.
@svteichman would you have the bandwidth to investigate if this is promising? This is fitting under both the null and the alternative (potentially best tested individually), and any g(). I think we could first confirm that the results of fitting are the same when Y is n x J vs when Y is p x J (aggregating over the p categories).
There would need to be a standard error adjustment (for I? Dy?) to ensure the Wald test stats are right. Presumably the same is true for the score.
This is lower priority than #173 .
In the course of pursuing faster null fitting for categorical covariates, it occurred to me that we may be able to speed up fitting under the alternative using a trivial technique:
In any discrete X setting with p categories I think we can immediately collapse Y from n x J to p x J, summing over the same X's. This may reduce computation especially for large n. This should work for any g().
I can't remember if scaling in n with radEmu is poor, but this could be worth a try. There's no need to keep rows as distinct, as any time they appear in the likelihood there is going to be aggregation over common X's.
@svteichman would you have the bandwidth to investigate if this is promising? This is fitting under both the null and the alternative (potentially best tested individually), and any g(). I think we could first confirm that the results of fitting are the same when Y is n x J vs when Y is p x J (aggregating over the p categories).
There would need to be a standard error adjustment (for
I?Dy?) to ensure the Wald test stats are right. Presumably the same is true for the score.This is lower priority than #173 .