Paul Allison discusses zero inflated vs negative binomial models in a post I stumbled across recently. Also William Greene and Paul go back and forth on some technical distinctions and nuances (which may be quite important) in the comments.
http://m.statisticalhorizons.com/?url=http%3A%2F%2Fstatisticalhorizons.com%2Fzero-inflated-models
"In all data sets that I've examined, the negative binomial model fits much better than a ZIP model, as evaluated by AIC or BIC statistics. And it's a much simpler model to estimate and interpret. So if the choice is between ZIP and negative binomial, I'd almost always choose the latter."
"But what about the zero-inflated negative binomial (ZINB) model? It's certainly possible that a ZINB model could fit better than a conventional negative binomial model regression model. But the latter is a special case of the former, so it's easy to do a likelihood ratio test to compare them (by taking twice the positive difference in the log-likelihoods). In my experience, the difference in fit is usually trivial..."
"So next time you're thinking about fitting a zero-inflated regression model, first consider whether a conventional negative binomial model might be good enough. Having a lot of zeros doesn't necessarily mean that you need a zero-inflated model."
No comments:
Post a Comment