An outlier-resistant indicator of anomalies among inter-laboratory comparison data with associated uncertainty

TL;DR

This paper introduces a new robust statistical method, the pairwise median scaled difference (MSD), for detecting anomalies in heteroscedastic inter-laboratory comparison data, demonstrating improved resistance to outliers and better detection power.

Contribution

The paper proposes the MSD statistic, providing its distribution, critical values, and a bootstrap method for heteroscedastic data, enhancing anomaly detection in inter-laboratory studies.

Findings

MSD outperforms previous methods in anomaly detection.

MSD shows high resistance to multiple outliers.

Approximate critical values facilitate routine application.

Abstract

A new robust pairwise statistic, the pairwise median scaled difference (MSD), is proposed for the detection of anomalous location/uncertainty pairs in heteroscedastic interlaboratory study data with associated uncertainties. The distribution for the IID case is presented and approximate critical values for routine use are provided. The determination of observation-specific quantiles and p-values for heteroscedastic data, using parametric bootstrapping, is demonstrated by example. It is shown that the statistic has good power for detecting anomalies compared to a previous pairwise statistic, and offers much greater resistance to multiple outlying values.