Open azev77 opened 4 years ago
Good ideas. Several of these are both easy and useful. I would probably recommend starting with Kuiper and Pearson's chisquare. Also, Shapiro-Wilk sounds useful, but I am not sure about how tricky it is to code.
There exists a transformation for W statistics that approximates it to normal distribution, see https://link.springer.com/article/10.1007/BF01891203.
@andreasnoack it looks like you have some really nice code for the Doornik-Hansen test for multivariate normality. Would it be possible for it to be ported here?
Sure. I'd be happy to move it here.
Also: PowerLaws.jl: Vuong's (1989) test for non-nested model selection, Clarke's (2007) test for non-nested model selection, Kolmogorov Smirnov test
Pingouin.jl: Levene's test & Bartlett's test for equality of variances Shapiro-Wilk, Shapiro-Francia, Anderson-Darling test Mauchly and JNS test for sphericity Mann-Whitney U Test (= Wilcoxon rank-sum test)
Is there any progress on this?
We have included several new tests recently. If you're interested in some of them which are still missing, help is welcome!
This package already has some nice options.
For general distributions
https://discourse.julialang.org/t/multivariate-normality-tests/8384/5
Tests for univariate normality:
For specific distributions:
These tests can be nicely combined to study goodness of fit:
Note the pvalue for AD > ApproxKSTest > ExactKSTest
It would be great if it was possible to automate test properties. This paper does a monte carlo simulation to compare the power of eight tests for normality.
@schrimpf does simulations to explore power here and here.