def mquantiles_cimj(data, prob=[<NUM_LIT>,<NUM_LIT>,<NUM_LIT>], alpha=<NUM_LIT>, axis=None):
alpha = min(alpha, <NUM_LIT:1>-alpha)<EOL>z = norm.ppf(<NUM_LIT:1>-alpha/<NUM_LIT>)<EOL>xq = mstats.mquantiles(data, prob, alphap=<NUM_LIT:0>, betap=<NUM_LIT:0>, axis=axis)<EOL>smj = mjci(data, prob, axis=axis)<EOL>return (xq - z * smj, xq + z * smj)<EOL>
Computes the alpha confidence interval for the selected quantiles of the
data, with Maritz-Jarrett estimators.

Parameters
----------
data : ndarray
    Data array.
prob : sequence
    Sequence of quantiles to compute.
alpha : float
    Confidence level of the intervals.
axis : integer
    Axis along which to compute the quantiles.
    If None, use a flattened array.