I guess there's a literature on this somewhere,
but it doesn't seem that trimming of tails
before regression ever caught on as standard practice
(unless there's a subdiscipline that does it all the
time, as a living refutation of this guess).
The key question to me is what is your underlying
problem? Worrying about long tails is often
best met by quantile or robust regression or using
transformations or non-identity link functions.
Far simpler and better supported than tinkering
with the tails...
Nick
[email protected]
Rijo John
>
> I have a data set with quite a few outliers. Suppose I am trimming my
> dependent variable 1% each from top and bottom using 1st and 99th
> percentiles. And I have the regression estimates before and after
> trimming. Let us also suppose that some of the variables that were
> significant before trimming turned out to be insignificant
> after trimming
> and/or viceversa.
>
> Is there a standard way by which one can decide how much percentage
> of data should be trimmed? Is a chow test for the equality of
> coefficients
> enough for this? I mean trim upto the point where the changes in
> coefficients becomes insignificant? Or is there any other
> standard way to
> do this?
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