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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 >> >> >> * >> * 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/ >> > > > > -- > 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/ > > * > * 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/ > -- 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/

**Follow-Ups**:**RE: st: zero truncated negative binomial (ztnb) with sampling weight***From:*"Kim, Seung Gyu" <sgkim@utk.edu>

**Re: st: zero truncated negative binomial (ztnb) with sampling weight***From:*Steve Samuels <sjsamuels@gmail.com>

**References**:**st: zero truncated negative binomial (ztnb) with sampling weight***From:*"Kim, Seung Gyu" <sgkim@utk.edu>

**Re: st: zero truncated negative binomial (ztnb) with sampling weight***From:*Steve Samuels <sjsamuels@gmail.com>

**RE: st: zero truncated negative binomial (ztnb) with sampling weight***From:*"Kim, Seung Gyu" <sgkim@utk.edu>

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