6.3.4. statsmodels.sandbox.distributions.extras¶

Various extensions to distributions

• skew normal and skew t distribution by Azzalini, A. & Capitanio, A.

• Gram-Charlier expansion distribution (using 4 moments),

• distributions based on non-linear transformation - Transf_gen - ExpTransf_gen, LogTransf_gen - TransfTwo_gen

  (defines as examples: square, negative square and abs transformations)

  • this versions are without __new__

• mnvormcdf, mvstdnormcdf : cdf, rectangular integral for multivariate normal distribution

TODO: * Where is Transf_gen for general monotonic transformation ? found and added it * write some docstrings, some parts I don’t remember * add Box-Cox transformation, parameterized ?

this is only partially cleaned, still includes test examples as functions

main changes * add transf_gen (2010-05-09) * added separate example and tests (2010-05-09) * collect transformation function into classes

>>> logtg = Transf_gen(stats.t, np.exp, np.log,
                numargs = 1, a=0, name = 'lnnorm',
                longname = 'Exp transformed normal',
                extradoc = '
distribution of y = exp(x), with x standard normal'
                'precision for moment andstats is not very high, 2-3 decimals')
>>> logtg.cdf(5, 6)
0.92067704211191848
>>> stats.t.cdf(np.log(5), 6)
0.92067704211191848

>>> logtg.pdf(5, 6)
0.021798547904239293
>>> stats.t.pdf(np.log(5), 6)
0.10899273954837908
>>> stats.t.pdf(np.log(5), 6)/5.  #derivative
0.021798547909675815