scipy.stats._continuous_distns:truncpareto_gen

Notes

The probability density function for truncpareto is:

for $b\ne 0$, $c>1$ and $1\le x\le c$.

truncpareto takes b and c as shape parameters for $b$ and $c$.

Notice that the upper truncation value $c$ is defined in standardized form so that random values of an unscaled, unshifted variable are within the range [1, c]. If u_r is the upper bound to a scaled and/or shifted variable, then c = (u_r - loc) / scale. In other words, the support of the distribution becomes (scale + loc) <= x <= (c*scale + loc) when scale and/or loc are provided.

The fit method assumes that $b$ is positive; it does not produce good results when the data is more consistent with negative $b$.

truncpareto can also be used to model a general power law distribution with PDF:

for $a\ne 0$ and $0<l<x<h$. Suppose $a$, $l$, and $h$ are represented in code as a, l, and h, respectively. In this case, use truncpareto with parameters b = -a, c = h / l, scale = l, and loc = 0.

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