I have problems coming up with a folder name and location for maximum
likelihood models based on a variety of distributions.
The structure of the models will be similar to `discrete` which are
all based on explicitly coded likelihood models.
However, `discrete` has discrete in the name and adding other models
for example for continuous on (0, 1) interval or on positive real
line is a misnomer.
Some non-discrete models that I would like to have in statsmodels are
- those used for survival and lifetime applications but without censoring first,
- and flexible models based on 3 or 4 parameter distributions when we
want to have more control over skew and kurtosis
- mixed discrete and continuous distribution like zero-inflated beta
We have currently also statsmodels.miscmodel but that is mainly for
prototype models and as testing ground for `GenericLikelihoodModels`.
I didn't manage to come up with a good directory name, only something
Any better ideas?
I'm currently trying to go into two opposite directions
- allow inference and similar under misspecification, "all models are
wrong" but we can still use misspecification robust methods
- add more support for modelling the "right" distribution for when we
are interested in more than mean parameters and robust inference
This started with count models for which we have a resonable good
selection (although still missing several basic models).
And as link between the two: specification tests and diagnostics to
figure out how wrong our model is and in which direction.