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#1




Target variable = counts. What log link?
If the target variable is unitvalued for something such as claim counts, I know I should use either a Poisson/Negative binomial distribution. What would be an argument for using either Poisson or Negative Binomial?
Also, what log link functions should I use and why? Thanks for any help 
#2




Log is one of the link functions to be used for continuous right skewed and Poisson data. They're not all log link functions. Logit is the most commonly used for a 0 to 1 "probability of an event" type of target variable(in the material we've been presented anyway)
I'm not sure when to use negative binomial or a nonlog link on a Poisson. This is just my first attempt and I'm unsure whether I'll pass. If it's something that complicated and it's not spelled out I'll probably lose a lot of point(Hopefully that would be most of us) 
#3




Quote:
If mean is less than variance (overdispersion), use quasipoisson or negative binomial. 
#4




Thanks!

#6




Quote:
There are two ways to answer your problem. One is practical, fit both models and then test which fits better. Second, statistically test if you can reject the null hypothesis: mu = sigma.
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#7




Quote:
I just ran some test simulations in R that bore that out. Seems like we are talking about different things. *Update. Hmmm, maybe when you say "the variance would be almost 0 regardless of the true model", you mean variance in the accounting sense; the difference between what we see for mean and what we see for variance. Not the variance of the sample data. If that's the case then I agree with your paragraph #1 but it didn't address my concern. Your second and third paragraph do, though. I can picture how I'd pull #2 off, especially in the context of this exam. #3 I'm having a hard time picturing. Maybe I need sleep. Thanks for the insight Last edited by Relmiw; 12102019 at 11:40 PM.. 
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