bundles / scipy latest / scipy / stats / _stats_py / expectile

function

`scipy.stats._stats_py:expectile`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_stats_py.py:10519

Signature

def expectile(a, alpha = 0.5, *, weights = None, axis = None, nan_policy = propagate, keepdims = False)

Summary

Compute the expectile at the specified level.

Extended Summary

Expectiles are a generalization of the expectation in the same way as quantiles are a generalization of the median. The expectile at level alpha = 0.5 is the mean (average). See Notes for more details.

Parameters

a : array_like

Array containing numbers whose expectile is desired.

alpha : float, default: 0.5

The level of the expectile; e.g., alpha=0.5 gives the mean.

weights : array_like, optional

An array of weights associated with the values in a. The weights must be broadcastable to the same shape as a. Default is None, which gives each value a weight of 1.0. An integer valued weight element acts like repeating the corresponding observation in a that many times. See Notes for more details.

axis : int or None, default: 0

If 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, a ValueError will be raised.

nan_policy='raise' and nan_policy='omit' are not supported by lazy backends, and nan_policy='omit' is not compatible with MArray input. For multidimensional input, nan_policy='omit' is supported only by the NumPy backend. Instead, mask NaN values using MArray; and use the default nan_policy. See Notes for support information.

keepdims : bool, default: False

If 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

expectile : ndarray: The empirical expectile at level alpha.

Notes

In general, the expectile at level $α$ of a random variable $X$ with cumulative distribution function (CDF) $F$ is given by the unique solution $t$ of:

α E ((X - t)_{+}) = (1 - α) E ((t - X)_{+}) .

Here, $(x)_{+} = max (0, x)$ is the positive part of $x$ . This equation can be equivalently written as:

α \int_{t}^{\infty} (x - t) d F (x) = (1 - α) \int_{- \infty}^{t} (t - x) d F (x) .

The empirical expectile at level $α$ (alpha) of a sample $a_{i}$ (the array a) is defined by plugging in the empirical CDF of a. Given sample or case weights $w$ (the array weights), it reads $F_{a} (x) = \frac{1}{\sum _{i} w _{i}} \sum_{i} w_{i} 1_{a_{i} \leq x}$ with indicator function $1_{A}$ . This leads to the definition of the empirical expectile at level alpha as the unique solution $t$ of:

α i = 1 \sum n w_{i} (a_{i} - t)_{+} = (1 - α) i = 1 \sum n w_{i} (t - a_{i})_{+} .

For $α = 0.5$ , this simplifies to the weighted average. Furthermore, the larger $α$ , the larger the value of the expectile.

As a final remark, the expectile at level $α$ can also be written as a minimization problem. One often used choice is

argmin_{t} E (∣ 1_{t \geq X} - α ∣ (t - X)^{2}) .

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.

Examples

import numpy as np
from scipy.stats import expectile
a = [1, 4, 2, -1]

✓

expectile(a, alpha=0.5) == np.mean(a)
expectile(a, alpha=0.2)
expectile(a, alpha=0.8)

✗

np.float64(2.571428571428571)

weights = [1, 3, 1, 1]

✓

expectile(a, alpha=0.8, weights=weights)

✗

np.float64(3.3333333333333335)

Aliases

scipy.stats.expectile