The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.

Machine Learning Resources

What is overdispersion in Poisson Regression, and what are alternate specifications for when it is present?

Bookmark this question

The poisson distribution is specified by one parameter lambda that represents both the mean and variance of the distribution. Thus, it assumes that the mean and variance are roughly the same. If that is not the case, usually when the variance is larger than the mean, overdispersion occurs.

When overdispersion is present, using Poisson regression can produce unreliable conclusions, as the underlying distribution might not be Poisson. If the assumption of the mean and variance being equal is not met, it is common to model the data using the Negative Binomial distribution with a dispersion parameter, which can be thought of as analogous to the sigma parameter that measures the spread of a normal distribution. 

Leave your Comments and Suggestions below:

Please Login or Sign Up to leave a comment

Partner Ad  

Find out all the ways
that you can

Explore Questions by Topics

Partner Ad

Learn Data Science with Travis - your AI-powered tutor |