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What is overdispersion in Poisson?
An assumption that must be fulfilled on Poisson distribution is the mean value of data equals to the variance value (or so- called equidispersion). If the variance value is greater than the mean value, it is called overdispersion. To handle overdispersion, the generalized Poisson regression model can be employed.
What is overdispersion in GLMM?
Overdispersion is an important concept in the analysis of discrete data. Overdispersion occurs because the mean and variance components of a GLM are related and depends on the same parameter that is being predicted through the independent vector.
When does overdispersion occur in a Poisson regression?
One feature of the Poisson distribution is that the mean equals the variance. However, over- or underdispersion happens in Poisson models, where the variance is larger or smaller than the mean value, respectively. In reality, overdispersion happens more frequently with a limited amount of data.
Is there such a thing as overdispersion in GLM?
Overdispersion occurs because the mean and variance components of a GLM are related and depends on the same parameter that is being predicted through the independent vector. There is no such thing as overdispersion in ordinary linear regression. In a linear regression model
How to check for over dispersion in a model?
Over-dispersion is a problem if the conditional variance (residual variance) is larger than the conditional mean. One way to check for and deal with over-dispersion is to run a quasi-poisson model, which fits an extra dispersion parameter to account for that extra variance. Now let’s fit a quasi-Poisson model to the same data.
Can you explain the Possion and quasi Poisson in GLM?
Hi Am also playing with the possion and quasi poisson in glm. I have found that the parameter fitting is identical using both families. It is only the dispersion parameter that changes. Can anyone explain this? That’s what quasi poisson is.