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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: RE: RE: Negative Binomial Models |

Date |
Thu, 14 Oct 2004 01:21:56 +0100 |

Are we talking about #bins or max(freq in bin) ? Nick n.j.cox@durham.ac.uk > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of MacDonald, > John > Sent: 14 October 2004 00:58 > To: statalist@hsphsun2.harvard.edu > Subject: st: RE: RE: Negative Binomial Models > > > Thanks Nick. > > John > > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Nick Cox > Sent: Wednesday, October 13, 2004 4:44 PM > To: statalist@hsphsun2.harvard.edu > Subject: st: RE: Negative Binomial Models > > > Should or should not? > > I'd like to hear the grounds for that. I find it > difficult to believe that any such criterion tenet > aquam. You should use a negative binomial distribution > whenever fitted probabilities are close to observed > probabilities. > > The underlying frequencies are not that germane. > But if the total number of individuals in the sample > is large, at least some cell frequencies are likely > to be. In fact, to get a good handle on probabilities > in the tail, a very large sample is a really good idea. > > I'm interested in comments on this. If you type > > . ssc type nbfit.hlp > > there's a worked example in which the peak frequency > was 807. > > Nick > n.j.cox@durham.ac.uk > > > -----Original Message----- > > From: Nick Cox > > Sent: 14 October 2004 00:38 > > To: 'statalist@hsphsun2.harvard.edu' > > Subject: RE: Negative Binomial Models > > > > > > > > > > Nick > > n.j.cox@durham.ac.uk > > > > > -----Original Message----- > > > From: owner-statalist@hsphsun2.harvard.edu > > > [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of > MacDonald, > > > John > > > Sent: 14 October 2004 00:23 > > > To: statalist@hsphsun2.harvard.edu > > > Subject: st: Negative Binomial Models > > > > > > > > > Is there a maximum number of counts one should not exceed > > > when using the negative binomial model? Even if there is a > > > adjustment for exposure (e.g., rate of homicides in a city). > > > I have a data set with over 200 homicides in some cities and > > > I saw a reference in an article of American Journal of > > > Sociology to Maddala's 1977 econometrics textbook that said > > > you should exceed 50 counts. Is this true? > > > > > > > > > > > > -------------------- > > > > > > This email message is for the sole use of the intended > > > recipient(s) and > > > may contain privileged information. Any unauthorized review, use, > > > disclosure or distribution is prohibited. If you are not > > the intended > > > recipient, please contact the sender by reply email and > > > destroy all copies > > > of the original message. > > > > > > > > > > > > * > > > * For searches and help try: > > > * http://www.stata.com/support/faqs/res/findit.html > > > * http://www.stata.com/support/statalist/faq > > > * http://www.ats.ucla.edu/stat/stata/ > > > > > > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > -------------------- > > This email message is for the sole use of the intended > recipient(s) and > may contain privileged information. Any unauthorized review, use, > disclosure or distribution is prohibited. If you are not the intended > recipient, please contact the sender by reply email and > destroy all copies > of the original message. > > > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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