Which model can aid in accommodating underdispersion compared to the Poisson model?

The negative binomial model is specifically designed to address the limitations of the Poisson model when dealing with underdispersion in count data. Underdispersion occurs when the observed variance is less than what the Poisson model assumes (which is equal to the mean). The negative binomial model introduces an additional parameter that allows it to model variability in the data more flexibly, accommodating situations where the data may not fit the strict assumptions of the Poisson distribution.

By using the negative binomial model, researchers can account for the extra variability or unobserved heterogeneity in the data, thus improving the fit of the model to real-world scenarios where the Poisson model might be too restrictive. This model’s structure helps in better capturing the characteristics of count data that display underdispersion, thereby providing more reliable parameter estimates and predictions.

In contrast, the Poisson model does not account for underdispersion, as it assumes that the mean and variance are equal, which is often not the case in real data. The linear regression model is typically used for continuous outcomes rather than counts, and the ARIMA model is more suited for time-series data rather than modeling counts or addressing underdispersion directly. Thus, the negative binomial model stands out as the most