I am not sure of the point you are making. In particular, I don’t see why would anyone use those improper linear models. It’s not 1979 and we can easily fit a variety of linear models including robust ones. Under which circumstances would you prefer an improper linear model to other alternatives available?
You could use them when building a new model in a new field with experts but little data. But the point is not so much to use these models, but to note that they still outperform experts.
Seconded. Back when I studied this topic for my thesis, the conclusion was not that “improper linear models are great”, but more “experts suck”. And that’s because in cases of repeated predictions, a statistical model is at least going to be consistent, but experts will not be.
You could use them when building a new model in a new field with experts but little data.
Again, why would you use this particular model class instead of other alternatives?
they still outperform experts
That statement badly needs modifiers. I would suggest “some improper linear models sometimes outperform experts”. Note that there is huge selection bias here. Also, your link is from 1979, where is that “still” coming from?
I am not sure of the point you are making. In particular, I don’t see why would anyone use those improper linear models. It’s not 1979 and we can easily fit a variety of linear models including robust ones. Under which circumstances would you prefer an improper linear model to other alternatives available?
You could use them when building a new model in a new field with experts but little data. But the point is not so much to use these models, but to note that they still outperform experts.
Seconded. Back when I studied this topic for my thesis, the conclusion was not that “improper linear models are great”, but more “experts suck”. And that’s because in cases of repeated predictions, a statistical model is at least going to be consistent, but experts will not be.
Again, why would you use this particular model class instead of other alternatives?
That statement badly needs modifiers. I would suggest “some improper linear models sometimes outperform experts”. Note that there is huge selection bias here. Also, your link is from 1979, where is that “still” coming from?
Fair qualifiers.