Re: st: Get fitted values after locpoly (follow-up)

[Nick Cox <njcoxstata@gmail.com>]

I don't see that fitting at a smaller set of points will itself confer  
resistance to outliers. Also, these local polynomial fits are all more  
or less fancy ways of averaging and so none is especially suited to  
what appears to be your aim.
Furthermore, I doubt that you can extend local polynomial fits beyond  
the data used as the command(s) in question do not save anything  
useful for that purpose.

To discount outliers and to get an otherwise smooth curve I would use - 
glm- with an appropriate link (e.g. logarithmic) and cubic spline  
variables on the right-hand side.

Finally, it's my recollection that -locpoly- is a user-written command  
and has been superseded by -lpoly-, although the latter is not more  
useful for resistant-robust smoothing than the former. As a general  
point you are asked to flag where user-written commands you refer to  
come from.

Nick

On 21 Sep 2011, at 11:58, Tania Treibich <tania.treibich@gmail.com>  
wrote:

> Dear Stata List users
>
> I could get fitted values for my kernel regression using the at()
> option of lpoly instead of the n() option:
>
> locpoly inv_rate l_kap, at(l_kap) generate (yfitted) degree(3)
> width(1.5) noscatter
>
> This indeed computes the smoothing and creates the fitted value
> yfitted for all the values of l_kap. However,  it gives too much
> weight to outliers.
>
> Instead, I would like the kernel regression to be computed only on a
> limited number of points (as in the option n(50) ) BUT get the fitted
> (approximated) value for ALL observations.
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