scoringrules.crps_tt#

scoringrules.crps_tt(obs: ArrayLike, df: ArrayLike, location: ArrayLike = 0.0, scale: ArrayLike = 1.0, lower: ArrayLike = -inf, upper: ArrayLike = inf, *, backend: Backend = None) → ArrayLike#

Compute the closed form of the CRPS for the truncated t distribution.

It is based on the formulation for the generalised truncated and censored t distribution with lmass and umass set to zero.

Parameters:

obsarray_like: The observed values.
dfarray_like: Degrees of freedom parameter of the forecast distribution.
locationarray_like: Location parameter of the forecast distribution.
scalearray_like: Scale parameter of the forecast distribution.
lowerarray_like: Lower boundary of the truncated forecast distribution.
upperarray_like: Upper boundary of the truncated forecast distribution.

Returns:

crpsarray_like: The CRPS between tt(df, location, scale, lower, upper) and obs.

Examples

>>> import scoringrules as sr
>>> sr.crps_tt(0.0, 2.0, 0.1, 0.4, -1.0, 1.0)
0.10323007471747117

scoringrules.crps_tt

Contents

scoringrules.crps_tt#