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Re: st: zero truncated negative binomial (ztnb) with sampling weight


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: zero truncated negative binomial (ztnb) with sampling weight
Date   Fri, 14 May 2010 15:52:14 -0400

There's will be no difference in the estimate parameters, but if you
will have incorrect standard errors (usually much too small) if you do
not -svyset- your data and provide stratum/cluster information. I'm
curious: the denominator of your residual is not the SD of a negative
binomial variable (see any probability book or the manual reference on
-nbreg-); what is it?

SS

On Fri, May 14, 2010 at 3:27 PM, Kim, Seung Gyu <sgkim@utk.edu> wrote:
> Thanks for your information. I need to read them, but what is the major difference between by weighting using "pweight" option in ZTNB vs. -svyset-.
>
> SG
>
> ztnb LHS RHS [pweight= xx] , dispersion(mean)
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Steve Samuels
> Sent: Friday, May 14, 2010 3:18 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: zero truncated negative binomial (ztnb) with sampling weight
>
> The form of the weighted likelihood (independent data) is given in the
> Stata Manual reference for -ztnb-.  As you have probability weights,
> you probably have a complex survey design (clusters, strata). If so,
> you should -svyset-your data and use -svy: ztnb-. For survey data, the
> general survey form of likelihood estimating equations is shown in the
> "Variance Estimation" chapter of Stata's Survey Data Manual.
>
> -predict- following -svy: ztnb- will give two kinds of predictions
> ("n" and "cm") from which you can form residuals. Use the first if
> zero was a possible value that could not be observed for some reason;
> otherwise- if the data are inherently positive- use the second.
>
> You appear to want to standardize the residual in some way. I don't
> recognize the denominator in your residual so I cannot comment on it.
> I would guess, however, that the denominator appropriate for the
> non-truncated negative binomial will suffice for all practical
> purposes.
>
> Steve
>
> On Tue, May 11, 2010 at 2:45 PM, Kim, Seung Gyu <sgkim@utk.edu> wrote:
>> Dear all:
>>
>> I am struggling with ZTNB with sampling weight. The residuals of
>> "negative binomial regression" are calculated as
>> (y-yhat)/(1+yhat*alpha), but I could not find the residuals if it is
>> "truncated" and "weighted by sampling weight" at the same time. I would
>> appreciate if someone gives me the functional form of residuals or
>> loglikelihood function for ZTNB with sampling weight.
>>
>> FYI, log likelihood function of zero truncated negative binomial is
>> Y*ln(alpha*exp(xb)/(1+alpha*exp(xb))-ln(1+alpha*exp(xb))/alpha+ln
>> Gamma(y+1/alpha)-ln Gamma(y+1)-ln
>> Gamma(1/alpha)-ln(1-(1+alpha*exp(xb))^(-1/alpha).
>> Thanks.
>>
>> SG Kim
>> sgkim@utk.edu
>>
>>
>> *
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>
>
>
> --
> Steven Samuels
> sjsamuels@gmail.com
> 18 Cantine's Island
> Saugerties NY 12477
> USA
> Voice: 845-246-0774
> Fax:    206-202-4783
>
> *
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>
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>



-- 
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783

*
*   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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