I want to estimate the standard error of an estimate.
It depends on the rows and columns of a dataframe.
Two possibilities:
Make a bootstrap on the rows to estimate $se_{row}$.
Do the same with the columns to estimate $se_{col}$.
Then take the general standard error is $se_{tot} = \sqrt{se_{row}^2 + se_{col}^2}$
Do a double bootstrap on the rows and columns, and get the standard error this way
Which one is more sound ?
For info, the rows are items' difficulties and columns are judges.