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


From   Austin Nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Get fitted values after locpoly (follow-up)
Date   Wed, 21 Sep 2011 11:34:32 -0400

Partho Sarkar <partho.ss+lists@gmail.com> :
You can certainly run -locpoly- (findit locpoly) or -lpoly- on a
sample of randomly selected points, keep the "optimal" bandwidth
chosen, and then reestimate using that bandwidth on the full sample,
and predict out of sample as well.  But they do not do adaptive
bandwidths, if that is what you had in mind.

On Wed, Sep 21, 2011 at 11:25 AM, Partho Sarkar
<partho.ss+lists@gmail.com> wrote:
> Tania
>
> I think I see where you are coming from, and so just a quick pointer:
>
>  You are probably thinking in terms of  "kernel regression" (or local
> polynomial regression) as usually understood in the machine learning
> literature, in which the bandwidth is *optimally* selected (or
> "tuned") from  an available "training set" or "memory set" of (xi,yi)
> points, and *this bandwidth, together with the training set data*, can
> then be used to "predict" the y0 value at some previously "query"
> point x0 outside the training set.  [In a sense, you could say that
> the training set together with the bandwidht constitute the "model"].
>
> But this is clearly not how locpoly is set up.  The bandwidth is
> fixed-either by default or your choice.  And I am not sure, having
> only tried a canned example with the program once very briefly, if
> there is any scope to meaningfully partition the data into training
> and query sets, as I think you might have in mind.  The user interface
> certainly does not *explicitly* give the user such a choice. [But this
> can be clarified by those more familiar with this command.]  There may
> be possibly be a roundabout way to get an approximation to what I
> think you have in mind. But if I wanted to do the kind of kernel
> regression I mention above, I would (without knowing what other Stata
> programs may be available for this) go to R's CRAN archives.  I worked
> on this a few years ago, so let me know and I could try to dig up
> some of the sources, or just search CRAN.
>
> Hope this helps
>
> Partho

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