There is a long tradition of estimating the complement of
what you want ( Pr(x>=525) instead of Pr(x<525) ) in
survival analysis, where they are sometimes called the
Kaplan-Meier estimates of the survivor function. This,
however, does not use any smoothing. In essence it uses
the empirical distribution, which is always discrete.
In Stata you can use -sts list- and -sts graph- to get
these estimates. I would strongly recommend going with
these estimates unless you have very very good arguments
to do otherwise. This is a tried and test technology.
There is some devolpments in terms of the inverse of
what you want (What value of x has 37% of the
observations below) which does use smooting, see
-ssc desc hdquantile- by Nick Cox, and the references
in the helpfile of -hdquantile-.
Hope this helps,
Maarten
--- Conner Mullally <[email protected]> wrote:
> I have a puzzle that I cannot figure out. I have a data
> series that runs from 600 or so up to 2200. What I would
> like to do is estimate a kernel density using this series,
> and then evaluate the cumulative probabilities of different
> values within the range cited above. For example, I might
> have X=(500, 623, and 2300), which I use to estimate the
> kernel density. But then I would like to know the
> probability that x<525, as predicted by the results of the
> kernel estimation. I tried creating a new variable
> consisting of integers between 500 and 2200 and using the
> "at" option with this variable, but I think this changes the
> number of points used to estimate the kernel, which is
> something I do not want. Thanks in advance for the help!
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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