scipy.stats.mstats.trimmed_stde(a, limits=(0.1, 0.1), inclusive=(1, 1), axis=None)[source]

Returns the standard error of the trimmed mean along the given axis.


a : sequence

Input array

limits : {(0.1,0.1), tuple of float}, optional

tuple (lower percentage, upper percentage) to cut on each side of the array, with respect to the number of unmasked data.

If n is the number of unmasked data before trimming, the values smaller than n * limits[0] and the values larger than n * `limits[1] are masked, and the total number of unmasked data after trimming is n * (1.-sum(limits)). In each case, the value of one limit can be set to None to indicate an open interval. If limits is None, no trimming is performed.

inclusive : {(bool, bool) tuple} optional

Tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False).

axis : int, optional

Axis along which to trim.


trimmed_stde : scalar or ndarray