Hi, I'm reposting someone else's question:
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.
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