Zero Inflated models with missing values

Modeling
1

Hi,

I have a question regarding how to deal with missing values in zero-inflated models. In these kind of models, we have discrete values (zeros) which come from Bernoulli distribution and non zero values which come from either continuous or discrete distribution.
I saw the concept of marginalization in some forums. But I don’t understand how can I apply it for this kind of models. I’m grateful if anyone have any solution for this.

Thank you very much

2

Hi,

can’t you do multiple imputations where you also impute the missingness in the outcome?

3

Hi,
Thank you for responding soon. I’m just wondering how can I implement in Stan. Because my missing values contains zero as well as non-zeros. But I cannot specify it as a parameter in the parameter block since it contains discrete values. I would like to know if there’s way that I can implement it in Stan.

Thank you

4

Hi,

sorry, only done it with brms and the mice package. Here’s a good introduction:
https://cran.r-project.org/web/packages/brms/vignettes/brms_missings.html

But, if you have zeros and NAs you first need to decide if the zeros are actual zeros, and not NAs coded as zeros.

5

Hi,

Thank you very much. I appreciate your feedback. I’m interested in coding it using Stan.