Prof Brian Ripley wrote:
However, stats 901 or some such tells you that if the distributions have
even slightly longer tails than the normal you can get much better
estimates than OLS, and this happens even before a test of normality
rejects on a sample size of thousands.
Robustness of efficiency is much more important than robustness of
distribution, and I believe robustness concepts should be in stats 101.
(I was teaching them yesterday in the third lecture of a basic course,
albeit a graduate course.)
This is a very interesting discussion. So you are basically saying that
it's better to use robust regression methods, without having to worry
too much about the distribution of residuals, instead of using standard
methods and doing a lot of tests to check for normality? Did I get your
point?
Cheers,
Federico
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