Hi German,
the Poisson distribution can not only be viewed as an approximation to the binomial for rare events, but also as a representation of a data generating mechanism in its own right. The idea is that events occur independently at some fixed rate, e.g. 10 car accidents at a specific intersection a year. Notice that the rate is larger than one, and that the events are probably not independent. In Poisson regression this rate is parameterized, i.e. one would expect the rate of accidents to be dependent on the time of day.
HTH,
Maarten
-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of German Rodriguez
Sent: dinsdag 11 oktober 2005 20:56
To: statalist@hsphsun2.harvard.edu
Subject: RE: st: RE: Re: predict after Poisson
The Poisson distribution can be viewed as an approximation to the binomial
for rare events, typically large number of attempts with small probability
of success in each one. In that case the fitted count will rarely exceed the
number of attempts. But why use an approximation when Stata can do the right
thing?
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