bundles / scipy 1.17.1 / scipy / stats / _morestats / kstatvar
function
scipy.stats._morestats:kstatvar
source: /scipy/stats/_morestats.py :344
Signature
def kstatvar ( data , n = 2 , * , axis = None , nan_policy = propagate , keepdims = False ) Summary
Return an unbiased estimator of the variance of the k-statistic.
Extended Summary
Parameters
data: array_likeInput array.
n: int, {1, 2}, optionalDefault is equal to 2.
axis: int or None, default: NoneIf an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If
None, the input will be raveled before computing the statistic.nan_policy: {'propagate', 'omit', 'raise'}Defines how to handle input NaNs.
propagate: if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN.omit: NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN.raise: if a NaN is present, aValueErrorwill be raised.
keepdims: bool, default: FalseIf this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
Returns
kstatvar: floatThe
nth k-statistic variance.
Notes
Unbiased estimators of the variances of the first two k-statistics are given by
Beginning in SciPy 1.9, np.matrix inputs (not recommended for new code) are converted to np.ndarray before the calculation is performed. In this case, the output will be a scalar or np.ndarray of appropriate shape rather than a 2D np.matrix. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or np.ndarray rather than a masked array with mask=False.
Array API Standard Support
kstatvar has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.
==================== ==================== ==================== Library CPU GPU ==================== ==================== ==================== NumPy ✅ n/a CuPy n/a ✅ PyTorch ✅ ✅ JAX ✅ ✅ Dask ✅ n/a ==================== ==================== ====================
See
dev-arrayapifor more information.
See also
- kstat
Returns the n-th k-statistic.
- moment
Returns the n-th central moment about the mean for a sample.
Aliases
-
scipy.stats.kstatvar