# scipy.stats.burr#

scipy.stats.burr = <scipy.stats._continuous_distns.burr_gen object>[source]#

A Burr (Type III) continuous random variable.

As an instance of the rv_continuous class, burr 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(c, d, loc=0, scale=1, size=1, random_state=None) Random variates. pdf(x, c, d, loc=0, scale=1) Probability density function. logpdf(x, c, d, loc=0, scale=1) Log of the probability density function. cdf(x, c, d, loc=0, scale=1) Cumulative distribution function. logcdf(x, c, d, loc=0, scale=1) Log of the cumulative distribution function. sf(x, c, d, loc=0, scale=1) Survival function (also defined as 1 - cdf, but sf is sometimes more accurate). logsf(x, c, d, loc=0, scale=1) Log of the survival function. ppf(q, c, d, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, c, d, loc=0, scale=1) Inverse survival function (inverse of sf). moment(order, c, d, loc=0, scale=1) Non-central moment of the specified order. stats(c, d, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(c, d, loc=0, scale=1) (Differential) entropy of the RV. fit(data) Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments. expect(func, args=(c, d), 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(c, d, loc=0, scale=1) Median of the distribution. mean(c, d, loc=0, scale=1) Mean of the distribution. var(c, d, loc=0, scale=1) Variance of the distribution. std(c, d, loc=0, scale=1) Standard deviation of the distribution. interval(confidence, c, d, loc=0, scale=1) Confidence interval with equal areas around the median.

fisk

a special case of either burr or burr12 with d=1

burr12

Burr Type XII distribution

mielke

Mielke Beta-Kappa / Dagum distribution

Notes

The probability density function for burr is:

$f(x; c, d) = c d \frac{x^{-c - 1}} {{(1 + x^{-c})}^{d + 1}}$

for $$x >= 0$$ and $$c, d > 0$$.

burr takes c and d as shape parameters for $$c$$ and $$d$$.

This is the PDF corresponding to the third CDF given in Burr’s list; specifically, it is equation (11) in Burr’s paper [1]. The distribution is also commonly referred to as the Dagum distribution [2]. If the parameter $$c < 1$$ then the mean of the distribution does not exist and if $$c < 2$$ the variance does not exist [2]. The PDF is finite at the left endpoint $$x = 0$$ if $$c * d >= 1$$.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, burr.pdf(x, c, d, loc, scale) is identically equivalent to burr.pdf(y, c, d) / 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]

Burr, I. W. “Cumulative frequency functions”, Annals of Mathematical Statistics, 13(2), pp 215-232 (1942).

[3]

Kleiber, Christian. “A guide to the Dagum distributions.” Modeling Income Distributions and Lorenz Curves pp 97-117 (2008).

Examples

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


Calculate the first four moments:

>>> c, d = 10.5, 4.3
>>> mean, var, skew, kurt = burr.stats(c, d, moments='mvsk')


Display the probability density function (pdf):

>>> x = np.linspace(burr.ppf(0.01, c, d),
...                 burr.ppf(0.99, c, d), 100)
>>> ax.plot(x, burr.pdf(x, c, d),
...        'r-', lw=5, alpha=0.6, label='burr 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 = burr(c, d)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')


Check accuracy of cdf and ppf:

>>> vals = burr.ppf([0.001, 0.5, 0.999], c, d)
>>> np.allclose([0.001, 0.5, 0.999], burr.cdf(vals, c, d))
True


Generate random numbers:

>>> r = burr.rvs(c, d, 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()