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Re: st: Underdispersion in count data from a survery


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Underdispersion in count data from a survery
Date   Thu, 7 Oct 2010 19:48:22 -0400

--


Your analysis  of under-dispersion could also be incorrect. Your
estimated mean and variance have to be  weighted by the survey
weights.  Your predicted values have to come from the weighted Poisson
regression,  and your  test for under-dispersion isn't valid unless
you used -svy: reg- to do it.

After -svy: mean-, the following will give you the estimate of the
sample variance
****************************************
scalar var_count = e(N)*el(e(V_srs),1,1)
di var_count
*********************************************
On Thu, Oct 7, 2010 at 7:15 PM, Steve Samuels <sjsamuels@gmail.com> wrote:
> --
> You can use the -glm- command in Stata 9 with survey data (link(log)
> family(poisson)) and follow it by -suest- to get valid standard
> errors. See http://www.stata.com/statalist/archive/2007-06/msg01103.html.
>  You could also run -svy reg- on the log of (count +1).
> Under-dispersion is a concern for standard errors when your
> inferences are likelihood based. The survey-based commands will base
> standard errors on the sample design.
>
> The Poisson or regression  models for predicting or estimating  means
> might be useful even if the data are not Poisson distributed. You
> still have to check model diagnostics. See:
> http://www.stata.com/meeting/dcconf09/dc09_valliant.pdf  for some
> ideas.
>
> Steve
>
>
>
> On Thu, Oct 7, 2010 at 5:46 PM, Laurie Molina <molinalaurie@gmail.com> wrote:
>> Hello all,
>>
>> I have count data from a survey which comes from a complex survey
>> design (stratification, clusters, two stages, and probabilistic
>> design).
>>
>> The mean is 1.89 and the variance is 1.14.
>>
>> Fallowing "Essentials of count data regression" by Cameron and Trivedi
>> (1999)To test for underdispersion i ran the following auxiliary
>> regression:
>>
>> ((yi - muhati)^2 - yi)/muhati= alpha*muhati + ui
>>
>> where yi is the i observation of  original dependent count variable,
>> muhati is the i observation of the fitted values resulting from the
>> poisson coefficients estimated, ui is the error term, and the
>> objective is to test alpha= 0 Vs alpha<0.
>>
>> Whith a p-value of 1, the hypothesis of alpha < 0 cannot be rejected.
>>
>> So my question is... What now? My data is underdispersed so the
>> standard errors estimated when i run the Poisson regression are not
>> correct (even when the estimates of my coefficents are consistent).
>>
>> Is there any solution for this problem in Stata 9, that can be used
>> with survey data?
>>
>> Thank you all very much in advance.
>>
>> Laurie
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/help.cgi?search
>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>>
>

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