Jackknife Function

stamox.sample.jackknife_sample(data: ArrayLike) -> ArrayLike

Generates num_samples jackknife samples from data with replacement.

Parameters:

Name	Type	Description	Default
data	array-like	The original data.	required

Returns:

Type	Description
ArrayLike	An array of size (len(data)-1, len(data)) containing the jackknife samples.

Examples:

>>> import jax.numpy as jnp
>>> from stamox.functions import jackknife_sample
>>> data = jnp.arange(3)
>>> jackknife_sample(data)
Array([[1, 2],
        [0, 2],
        [0, 1]], dtype=int32)

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stamox.sample.jackknife(data: ArrayLike, call: Callable[..., ~ReturnValue]) -> PyTree

Computes the jackknife estimate of a given data set.

Parameters:

Name	Type	Description	Default
data	ArrayLike	The data set to be analyzed.	required
call	Callable[..., ReturnValue]	A function to be applied to each sample.	required

Returns:

Type	Description
PyTree	The jackknife estimate of the data set.

Examples:

>>> import jax.numpy as jnp
>>> from stamox.functions import jackknife
>>> data = jnp.arange(3)
>>> jackknife(data, lambda x: jnp.mean(x))
Array([1.5, 1. , 0.5], dtype=float32)