def mjci(data, prob=[<NUM_LIT>,<NUM_LIT:0.5>,<NUM_LIT>], axis=None):
def _mjci_1D(data, p):<EOL><INDENT>data = np.sort(data.compressed())<EOL>n = data.size<EOL>prob = (np.array(p) * n + <NUM_LIT:0.5>).astype(int_)<EOL>betacdf = beta.cdf<EOL>mj = np.empty(len(prob), float_)<EOL>x = np.arange(<NUM_LIT:1>,n+<NUM_LIT:1>, dtype=float_) / n<EOL>y = x - <NUM_LIT:1.>/n<EOL>for (i,m) in enumerate(prob):<EOL><INDENT>(m1,m2) = (m-<NUM_LIT:1>, n-m)<EOL>W = betacdf(x,m-<NUM_LIT:1>,n-m) - betacdf(y,m-<NUM_LIT:1>,n-m)<EOL>C1 = np.dot(W,data)<EOL>C2 = np.dot(W,data**<NUM_LIT:2>)<EOL>mj[i] = np.sqrt(C2 - C1**<NUM_LIT:2>)<EOL><DEDENT>return mj<EOL><DEDENT>data = ma.array(data, copy=False)<EOL>if data.ndim > <NUM_LIT:2>:<EOL><INDENT>raise ValueError("<STR_LIT>"<EOL>"<STR_LIT>" % data.ndim)<EOL><DEDENT>p = np.array(prob, copy=False, ndmin=<NUM_LIT:1>)<EOL>if (axis is None):<EOL><INDENT>return _mjci_1D(data, p)<EOL><DEDENT>else:<EOL><INDENT>return ma.apply_along_axis(_mjci_1D, axis, data, p)<EOL><DEDENT>
Returns the Maritz-Jarrett estimators of the standard error of selected
experimental quantiles of the data.

Parameters
----------
data: ndarray
    Data array.
prob: sequence
    Sequence of quantiles to compute.
axis : int
    Axis along which to compute the quantiles. If None, use a flattened
    array.