I am trying to predict a strictly positive, continuous, right-skewed variable (purchase value) given a set of categorical attributes.
The current popular options include:
1) log-transform the variable and run a (penalized) linear model. The issue is that the model is biased
2) run a poisson/negative-binomial (penalized) regression model because it deals with the bias, however it assumes an integer outcome.
Anyone who had more success with the former or the latter approach? The former is biased the latter loses information because of truncating values to integers but it is not clear to me when I should use one over the other.
Best Answer
To remove the bias, the solution is to use any generalized linear model. Poisson is just one of them but you can use many distributions.
A classical solution for continuous variables with the log link function is gamma regression that is a generalized linear model.
You can read more on this question : When to use gamma GLMs?