If 'loc', 'scale' and 'shape' are not specified they assume the default
values of '0', '1' and '0', respectively.
The GP distribution function for loc = \(u\), scale =
\(\sigma\) and shape = \(\xi\) is
$$G(x) = 1 - \left[ 1 + \frac{\xi (x - u )}{ \sigma } \right] ^ { - 1 /
\xi}$$
for \(1 + \xi ( x - u ) / \sigma > 0\)
and \(x > u\), where \(\sigma > 0\). If
\(\xi = 0\), the distribution is defined by continuity
corresponding to the exponential distribution.
By definition, the GP distribution models exceedances above a
threshold. In particular, the \(G\) function is a suited
candidate to model
$$\Pr\left[ X \geq x | X > u \right] = 1 - G(x)$$
for \(u\) large enough.
However, it may be usefull to model the "non conditional" quantiles,
that is the ones related to \(\Pr[ X \leq x]\). Using
the conditional probability definition, one have :
$$\Pr\left[ X \geq x \right] = \left(1 - \lambda\right) \left( 1 +
\xi \frac{x - u}{\sigma}\right)^{-1/\xi}$$
where \(\lambda = \Pr[ X \leq u]\).
When \(\lambda = 0\), the "conditional" distribution
is equivalent to the "non conditional" distribution.