Are there two-part models with too many zeros?

Are there two-part models with too many zeros?

Two-part models in R: continuous outcome with too many zeros. I am estimating a model where the outcome variable is continuous, more specifically a percentage in the form of a 0 to 1 range. This variable has one potential problem: many of its cases equal zero (around 40%).

When to analyze continuous outcomes with many zeros?

Prevention researchers often analyze nonnegative continuous outcomes that are skewed and contain many zero observations. Variables with these features can arise when investigators measure low-base-rate behaviors (e.g., bullying, substance use) or use measurement tools that cannot differentiate individuals with low standing on the target construct.

How to analyze skewed outcomes with many zeros?

Two approaches, Tobit regression and two-part models, are particularly useful methods for handling skewed nonnegative outcomes with several zero values. Familiarity with the issues and techniques we present may help researchers to make more informed analytic choices when confronted with such outcomes.

When do you have too many continuous variables?

I am estimating a model where the outcome variable is continuous, more specifically a percentage in the form of a 0 to 1 range. This variable has one potential problem: many of its cases equal zero (around 40%).

Is there such a thing as two part model estimation?

I have been searching for the appropriate technique to handle the problem, and found out that probably the most indicated would be the so called two-part model estimation – which is pretty much the joint application of a logit and a truncated estimation. However, I would like to check with you all:

Can a Poisson zero inflated model be used in regression?

Since it is not a count variable, I cannot employ a Poisson zero-inflated model. I have been searching for the appropriate technique to handle the problem, and found out that probably the most indicated would be the so called two-part model estimation – which is pretty much the joint application of a logit and a truncated estimation.