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This question already has an answer here:

As I am browsing the resources online, I encounter as least three ways to detect overdispersion after fitting a Poisson regression model.

1) A regression approach proposed by Cameron and Trivedi, 1990

2) The Lagrange Multiplier test

3) A third test proposed by Greene found below

http://people.tamu.edu/~b-wood/Maximum%20Likelihood/RLesson%208.htm

I found literature references to 1) and 2), but am having a hard time finding anything related to 3). Since every test has it own assumptions and limitations, would anyone shine some light on what passing/failing each test entails?

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marked as duplicate by kjetil b halvorsen, Michael Chernick, whuber Mar 29 at 17:58

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

  • $\begingroup$ Other possible dups: stats.stackexchange.com/questions/256327/…, stats.stackexchange.com/questions/38177/… $\endgroup$ – kjetil b halvorsen Mar 28 at 22:02
  • $\begingroup$ Thank you @kjetilbhalvorsen! I have read the other two links, and I don't believe that my question is a duplicate of either. My question was very specific -- I was asking for literature references of the "Greene" approach to overdispersion tests, which is not mentioned by the other two questions, their answers or their references. I have edited the title of my question. Hopefully this would give potential answerers a clearer picture. Thanks! $\endgroup$ – Ye Tian Mar 30 at 23:23
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    $\begingroup$ Answer must be somewhere in this search: scholar.google.com/… $\endgroup$ – kjetil b halvorsen Apr 5 at 19:57