Zero Inflated Beta Regression

How can we check that the proportions are inflated at zero and one while using Beta Regression?

Beta regression assumes that the predicted variable is in $$(0, 1)$$, so exact $$0$$ and $$1$$ are excluded from the interval. So if you have any exact zeros, beta distribution and regression would not be appropriate and you would need to use something like the Hurdle model to account for them.

You may also be interested in reading the Dealing with 0,1 values in a beta regression thread.

• I somehow disagree... A hurdle model would assume a truncated Poisson/NB/Gaussian/etc. for the counts model so it would not allow for zeros either. So a hurdle model with Logistic/Beta for components should still be valid. No? Commented May 9, 2021 at 13:29
• We could run a hurdle model with Logistic/Beta for components should still be valid instead of a standard Logistic/Truncated_Poisson (that is the usual). Commented May 10, 2021 at 9:09
• @usεr11852 the only thing I'm saying is that the standard Bete regression model & common implementations would not work for data with exact 1 or 0.
– Tim
Commented May 10, 2021 at 9:34
• I think that the name "zero-inflated beta regression" is the source of the confusion here. While in "zero-inflated Poisson" you really add "excess" zeros to the zeros from the Poisson distribution, the same is not true for "zero-inflated beta". As beta regression cannot accomodate any zeros - as correctly pointed out by Tim - there is nothing to inflate. A better name would be "hurdle" or "two-part" model when adding zeros (and/or ones) to a standard beta regression. This is straightforward as correctly pointed out by user11852. Commented May 10, 2021 at 9:38
• @usεr11852 agree. I focused on the body of the question, asking about "inflated zeros in beta regression", but now I see how someone could focus more on the title of the question, than the body. Seems like the question is ambiguous. Edited for clarity.
– Tim
Commented May 10, 2021 at 9:56