pingouin.madmedianrule

pingouin.
madmedianrule
(a)[source] Robust outlier detection based on the MADmedian rule.
 Parameters
 aarraylike
Input array. Must be onedimensional.
 Returns
 outliers: boolean (same shape as a)
Boolean array indicating whether each sample is an outlier (True) or not (False).
Notes
The MADmedianrule will refer to declaring \(X_i\) an outlier if
\[\frac{\left  X_i  M \right }{\text{MAD}_{\text{norm}}} > K,\]where \(M\) is the median of \(X\), \(\text{MAD}_{\text{norm}}\) the normalized median absolute deviation of \(X\) (see
pingouin.mad()
), and \(K\) is the square root of the .975 quantile of a \(X^2\) distribution with one degree of freedom, which is roughly equal to 2.24.References
 1
Hall, P., Welsh, A.H., 1985. Limit theorems for the median deviation. Ann. Inst. Stat. Math. 37, 27–36. https://doi.org/10.1007/BF02481078
 2
Wilcox, R. R. Introduction to Robust Estimation and Hypothesis Testing. (Academic Press, 2011).
Examples
>>> import pingouin as pg >>> a = [1.09, 1., 0.28, 1.51, 0.58, 6.61, 2.43, 0.43] >>> pg.madmedianrule(a) array([False, False, False, False, False, True, False, False])