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Re: st: testing for bimodality in survey data

From   Joerg Luedicke <>
Subject   Re: st: testing for bimodality in survey data
Date   Wed, 9 Nov 2011 17:07:52 -0500

I am not sure what "testing" is supposed to mean in this context, but
if you want to explore the possibility of a multimodal distribution
you could indeed go for a non-parametric density estimation. I
recommend using Ben Jann's -kdens- (available from SSC, -findit
kdens-), which is a quite powerful package and supports probability
weights. I would also recommend using an adaptive kernel estimate, as
this is usually the best kernel estimate when dealing with multimodal
data (at least in my experience). What you could do in addition is
checking whether the multimodality is due to distributional mixtures
(which is often the case when you find more than one mode). For
example, say you find your distribution being bimodal, you could fit a
2-component mixture model to estimate the underlying parameters of the
mixed distributions via maximum likelihood (to do this you could use
-fmm- which is also available from SSC; if the model does not converge
make sure you provide starting values; for Gaussian mixtures you could
use the modes from the kernel estimate and guess the variance). You
could also check how well the (in this case) 2 distributions can be
separated with using an entropy measure which you could calculate with
-fmmlc-, also available from SSC.


On Wed, Nov 9, 2011 at 1:50 PM, Dana Shills <> wrote:
> Hello:
> I am using survey data on firms in Ghana. The survey methodology uses stratified random sampling and I have the probability weights. I want to be able to plot a distribution of firm sizes (incorporating the weights) and test for bimodality in the firm size distribution. I looked at the "adgakern" program but I don't think it allows for survey weights. Could someone please point me to what commands I should be looking at?
> Thank you for your time.
> Regards,Dana
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