st: Getting standard errors and confidence intervals after kernel density estimation

[Sowmya Varadharajan <sv49@cornell.edu>]

Hi,

I am trying to do a kernel density estimation of a y ( a 0-1 variable) on 
x1. This generates Graph1. I also did an estimation on y on x2 and 
generated graph2. I used kernreg2 for both these estimations.

Now, I would also like to bootstrap confidence intervals around the graph 
and subsequently test the two distributions from graph 1 and 2 (to see if 
they are statistically different in the relevant range) . Unfortunately, 
kernreg2 does not give the non-parametric standard errors. I tried 
bootstrapping nevertheless, and this is the output that I get.
Bootstrap statistics

Variable |   Reps   Observed       Bias   Std. Err.   [95% Conf. Interval]
---------+-------------------------------------------------------------------
  klnpce |    100   10.69125   .5342394   .9190264    8.867703   12.5148  (N)
         |                                            		     9.449879 
13.2954  (P)
         |                                            		     9.095177 
11.76517 (BC)
-----------------------------------------------------------------------------
                              N = normal, P = percentile, BC = bias-corrected


First I would like to draw confidence intervals for the entire function, 
and then bootstrap the confidence intervals and am not sure how to do it. I 
was wondering if anyone had faced this problem, and could help me out.

Thanks.

Sowmya 
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