Which model can address underdispersion relative to the Poisson model?

To address underdispersion relative to the Poisson model, the appropriate choice is the hurdle model. Underdispersion occurs when the data exhibit less variability than predicted by the Poisson distribution, which can lead to an underestimation of the true variability in the data.

The hurdle model is particularly effective in handling count data that tend to have an excessive number of zeros and less variation in positive counts. It does so by splitting the modeling process into two parts: one for the count of zeros (the "hurdle" part) and another for the positive counts. This dual approach allows the hurdle model to effectively manage scenarios where the counts are generally lower, particularly when the data do not meet the overdispersion assumptions that models like the negative binomial may utilize.

In contrast, while the negative binomial model is often used to handle overdispersion, it may not specifically cater to underdispersion scenarios. Poisson regression assumes that the mean equals the variance, which is not suitable for datasets with underdispersion. Similarly, though zero-inflated models can handle excess zeros, they do not address underdispersion directly either, as their focus is primarily on modeling the excess of zeros rather than the variance issue itself.

In conclusion, the