def METHOD_NAME(data):
    """Performs jackknife resampling on numpy arrays.
    Jackknife resampling is a technique to generate 'n' deterministic samples
    of size 'n-1' from a measured sample of size 'n'. Basically, the i-th
    sample, (1<=i<=n), is generated by means of removing the i-th measurement
    of the original sample. Like the bootstrap resampling, this statistical
    technique finds applications in estimating variance, bias, and confidence
    intervals.
    Parameters
    ----------
    data : ndarray
        Original sample (1-D array) from which the jackknife resamples will be
        generated.
    Returns
    -------
    resamples : ndarray
        The i-th row is the i-th jackknife sample, i.e., the original sample
        with the i-th measurement deleted.
    References
    ----------
    .. [1] McIntosh, Avery. "The Jackknife Estimation Method".
        <https://arxiv.org/abs/1606.00497>
    .. [2] Efron, Bradley. "The Jackknife, the Bootstrap, and other
        Resampling Plans". Technical Report No. 63, Division of Biostatistics,
        Stanford University, December, 1980.
    .. [3] Jackknife resampling <https://en.wikipedia.org/wiki/Jackknife_resampling>
    """
    n = data.shape[0]
    if n <= 0:
        raise ValueError("data must contain at least one measurement.")
    resamples = np.empty([n, n - 1])
    for i in range(n):
        resamples[i] = np.delete(data, i)
    return resamples