statsmodels / statsmodels/statsmodels
Diagnostics, Specification tests after discrete and GLM
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Description
(intended as umbrella issue)
We need the diagnostics and specification tests for all models, similar to stats.dignostics, stats.influence_outliers and graphics.regressionplots which is currently mainly (only) for OLS
several textbooks list hypothesis tests, plots and descriptive measures for the results for this, GLM, Logit, Probit
- regressionplots: seems to apply in a very similar way to discrete models including GLM
- influence_outliers: several statistics are similar or unchanged, but set is smaller
mostly based on residuals - diagnostics: specification tests
related issues:
???
Starting with diagnostics,
I have some tests for heteroscedasticity after binary models, Logit and Probit, and after Poisson in uncommitted scripts.
I haven't checked yet how much we can reuse current tests like the het_xxx and acorr_xxx
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the existing stats.diagnostics, stats.influence_outliers, and graphics.regressionplots implementations for OLS, then review the uncommitted heteroscedasticity tests for binary models, Logit, Probit, and Poisson. Determine which current het_xxx and acorr_xxx tests can be reused; done means an agreed, implemented diagnostics and specification-test scope for the additional models.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, testing
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100