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.