statsmodels.stats.robust_compare.TrimmedMean¶

class statsmodels.stats.robust_compare.TrimmedMean(data, fraction, is_sorted=False, axis=0)[source]¶

class for trimmed and winsorized one sample statistics

axis is None, i.e. ravelling, is not supported

Parameters:¶

data : array-like¶

The data, observations to analyze.

fraction : float in (0, 0.5)¶

The fraction of observations to trim at each tail. The number of observations trimmed at each tail is int(fraction * nobs)

is_sorted : boolean¶

Indicator if data is already sorted. By default the data is sorted along axis.

axis : int¶

The axis of reduce operations. By default axis=0, that is observations are along the zero dimension, i.e. rows if 2-dim.

Methods

reset_fraction(frac)	create a TrimmedMean instance with a new trimming fraction
ttest_mean([value, transform, alternative])	One sample t-test for trimmed or Winsorized mean

Properties

data_trimmed	numpy array of trimmed and sorted data
data_winsorized	winsorized data
mean_trimmed	mean of trimmed data
mean_winsorized	mean of winsorized data
std_mean_trimmed	standard error of trimmed mean
std_mean_winsorized	standard error of winsorized mean
var_winsorized	variance of winsorized data