SST Class¶
- class sstudentt.SST(mu, sigma, nu, tau)[source]¶
Creates an Instance of the Skewed Student T Distribution. In this parameterization the expectation equals mu and standard deviation equals sigma.
- Parameters
mu (scalar or array_like) – mu parameter
sigma (scalar or array_like) – sigma parameter
nu (scalar or array_like) – nu parameter
tau (scalar or array_like) – tau parameter
SST Methods¶
- SST.d(y)[source]¶
Density Function
- Parameters
y (scalar or array_like) – distribution values
- Returns
density at the specified y values
- Return type
array
- SST.p(q)[source]¶
Distribution Function
- Parameters
q (scalar or array_like) – value
- Returns
The probability that the SST distributed variable will take
a value less than or equal to q. :rtype: array
- SST.q(p)[source]¶
Quantile Function / Inverse CDF / Percent Point Function
- Parameters
p (scalar or array_like) – probabilities
- Returns
Quantile values corresponding to the specified probabilities.
- Return type
array
- SST.r(n=1)[source]¶
Draws Random Numbers which Follow the SST Distribution
- Parameters
n (int or tuple of return shape, optional) – sample size
- Returns
random sample drawn from the SST distribution
- Return type
array
Note
n is ignored if the distribution parameters are provided as arrays. In that case, a sample with the shape of the provided arrays will be drawn. i.e. n = 1.