Stein's paradox is bogus. Somebody needs to say that.
Here's one wikipedia example:
> Suppose we are to estimate three unrelated parameters, such as the US wheat yield for 1993, the number of spectators at the Wimbledon tennis tournament in 2001, and the weight of a randomly chosen candy bar from the supermarket. Suppose we have independent Gaussian measurements of each of these quantities. Stein's example now tells us that we can get a better estimate (on average) for the vector of three parameters by simultaneously using the three unrelated measurements.
No it is not bogus, you just don't know much stats apparently.
I remember back in 7th or 8th grade I asked my math teacher why we want to minimize the rms error rather than the sum of the absolute values of the errors. She couldn't give me a good answer, but the book All of Statistics does answer why (and under what circumstances) that is the right thing to do.
The general notion of compound risk is not specific to MSE loss. You can formulate it for any loss function, including L1 loss which you seem to prefer.
Steins paradox and James Stein estimator is just a special case for normal random variables and MSE loss of the more general theory of compound estimation, which is trying to find an estimator which can leverage all the data to reduce overall error.
This idea, compound estimation and James-Stein, is by now out-dated. Later came the invention of empirical Bayes estimation and the more modern bayesian hierarchical modelling eventually once we had compute for that.
One thing you can recover from EB is the James-Stein estimator, as a special case, in fact, you can design much better families of estimators that are optimal with respect to Bayes risk in compound estimation settings.
This is broadly useful in pretty much any situation where you have a large scale experiment where many small samples are drawn and similar stats are computed in parallel, or when the data has a natural hierarchical structure. For examples, biostats, but also various internet data applications.
so yeah, suggest to be a bit more open to ideas you dont know anything about. @zeroonetwothree is not agreeing with you here, they're pointing out that you cooked up an irrelevant "example" and then claim the technique doesnt make sense there. Of course, it doesnt, but thats not because the idea of JS isnt broadly useful.
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Another thing is that JS estimator can be viewed as an example of improving overall bias-variance by regularization, although the connection to regularization as most people in ML use it is maybe less obvious. If you think regularization isn't broadly applicable and very important... i've got some news for you.