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Module « scipy.stats »

Fonction jf_skew_t - module scipy.stats

Signature de la fonction jf_skew_t

def jf_skew_t(*args, **kwds) 

Description

help(scipy.stats.jf_skew_t)

Jones and Faddy skew-t distribution.

As an instance of the `rv_continuous` class, `jf_skew_t` object inherits from it
a collection of generic methods (see below for the full list),
and completes them with details specific for this particular distribution.

Methods
-------
rvs(a, b, loc=0, scale=1, size=1, random_state=None)
    Random variates.
pdf(x, a, b, loc=0, scale=1)
    Probability density function.
logpdf(x, a, b, loc=0, scale=1)
    Log of the probability density function.
cdf(x, a, b, loc=0, scale=1)
    Cumulative distribution function.
logcdf(x, a, b, loc=0, scale=1)
    Log of the cumulative distribution function.
sf(x, a, b, loc=0, scale=1)
    Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
logsf(x, a, b, loc=0, scale=1)
    Log of the survival function.
ppf(q, a, b, loc=0, scale=1)
    Percent point function (inverse of ``cdf`` --- percentiles).
isf(q, a, b, loc=0, scale=1)
    Inverse survival function (inverse of ``sf``).
moment(order, a, b, loc=0, scale=1)
    Non-central moment of the specified order.
stats(a, b, loc=0, scale=1, moments='mv')
    Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
entropy(a, b, loc=0, scale=1)
    (Differential) entropy of the RV.
fit(data)
    Parameter estimates for generic data.
    See `scipy.stats.rv_continuous.fit <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.fit.html#scipy.stats.rv_continuous.fit>`__ for detailed documentation of the
    keyword arguments.
expect(func, args=(a, b), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)
    Expected value of a function (of one argument) with respect to the distribution.
median(a, b, loc=0, scale=1)
    Median of the distribution.
mean(a, b, loc=0, scale=1)
    Mean of the distribution.
var(a, b, loc=0, scale=1)
    Variance of the distribution.
std(a, b, loc=0, scale=1)
    Standard deviation of the distribution.
interval(confidence, a, b, loc=0, scale=1)
    Confidence interval with equal areas around the median.

Notes
-----
The probability density function for `jf_skew_t` is:

.. math::

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

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

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

`jf_skew_t` takes :math:`a` and :math:`b` as shape parameters.

The probability density above is defined in the "standardized" form. To shift
and/or scale the distribution use the ``loc`` and ``scale`` parameters.
Specifically, ``jf_skew_t.pdf(x, a, b, loc, scale)`` is identically
equivalent to ``jf_skew_t.pdf(y, a, b) / scale`` with
``y = (x - loc) / scale``. Note that shifting the location of a distribution
does not make it a "noncentral" distribution; noncentral generalizations of
some distributions are available in separate classes.

References
----------
.. [1] M.C. Jones and M.J. Faddy. "A skew extension of the t distribution,
       with applications" *Journal of the Royal Statistical Society*.
       Series B (Statistical Methodology) 65, no. 1 (2003): 159-174.
       :doi:`10.1111/1467-9868.00378`

Examples
--------
>>> import numpy as np
>>> from scipy.stats import jf_skew_t
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate the first four moments:

>>> a, b = 8, 4
>>> mean, var, skew, kurt = jf_skew_t.stats(a, b, moments='mvsk')

Display the probability density function (``pdf``):

>>> x = np.linspace(jf_skew_t.ppf(0.01, a, b),
...                 jf_skew_t.ppf(0.99, a, b), 100)
>>> ax.plot(x, jf_skew_t.pdf(x, a, b),
...        'r-', lw=5, alpha=0.6, label='jf_skew_t pdf')

Alternatively, the distribution object can be called (as a function)
to fix the shape, location and scale parameters. This returns a "frozen"
RV object holding the given parameters fixed.

Freeze the distribution and display the frozen ``pdf``:

>>> rv = jf_skew_t(a, b)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of ``cdf`` and ``ppf``:

>>> vals = jf_skew_t.ppf([0.001, 0.5, 0.999], a, b)
>>> np.allclose([0.001, 0.5, 0.999], jf_skew_t.cdf(vals, a, b))
True

Generate random numbers:

>>> r = jf_skew_t.rvs(a, b, size=1000)

And compare the histogram:

>>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
>>> ax.set_xlim([x[0], x[-1]])
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()

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