`scipy.stats._continuous_distns:jf_skew_t_gen`

source: /scipy/stats/_continuous_distns.py :5688

Signature

   class     jf_skew_t_gen (    momtype    =  1   ,    a    =  None   ,    b    =  None   ,    xtol    =  1e-14   ,    badvalue    =  None   ,    name    =  None   ,    longname    =  None   ,    shapes    =  None   ,    seed    =  None     )

Members

Summary

Jones and Faddy skew-t distribution.

Extended Summary

%(before_notes)s

Notes

The probability density function for jf_skew_t is:

f (x; a, b) = C_{a, b}^{- 1} (1 + \frac{x}{( a + b + x ^{2} ) ^{1/2}})^{a + 1/2} (1 - \frac{x}{( a + b + x ^{2} ) ^{1/2}})^{b + 1/2}

for real numbers $a > 0$ and $b > 0$ , where $C_{a, b} = 2^{a + b - 1} B (a, b) (a + b)^{1/2}$ , and $B$ denotes the beta function (scipy.special.beta).

When $a < b$ , the distribution is negatively skewed, and when $a > b$ , the distribution is positively skewed. If $a = b$ , then we recover the t distribution with $2 a$ degrees of freedom.

jf_skew_t takes $a$ and $b$ as shape parameters.

%(after_notes)s

Aliases

scipy.stats._continuous_distns.jf_skew_t_gen