6.3.14. statsmodels.sandbox.distributions.transformed¶

A class for the distribution of a non-linear monotonic transformation of a continuous random variable

simplest usage: example: create log-gamma distribution, i.e. y = log(x),

where x is gamma distributed (also available in scipy.stats)

loggammaexpg = Transf_gen(stats.gamma, np.log, np.exp)

example: what is the distribution of the discount factor y=1/(1+x)

where interest rate x is normally distributed with N(mux,stdx**2)’)? (just to come up with a story that implies a nice transformation)

invnormalg = Transf_gen(stats.norm, inversew, inversew_inv, decr=True, a=-np.inf)

This class does not work well for distributions with difficult shapes,

e.g. 1/x where x is standard normal, because of the singularity and jump at zero.

Note: I’m working from my version of scipy.stats.distribution.

But this script runs under scipy 0.6.0 (checked with numpy: 1.2.0rc2 and python 2.4)

This is not yet thoroughly tested, polished or optimized

TODO:

• numargs handling is not yet working properly, numargs needs to be specified (default = 0 or 1)
• feeding args and kwargs to underlying distribution is untested and incomplete
• distinguish args and kwargs for the transformed and the underlying distribution - currently all args and no kwargs are transmitted to underlying distribution - loc and scale only work for transformed, but not for underlying distribution - possible to separate args for transformation and underlying distribution parameters
• add _rvs as method, will be faster in many cases

Created on Tuesday, October 28, 2008, 12:40:37 PM Author: josef-pktd License: BSD