Robust approaches for mean and variance outliers detection in Round Robin Tests

Large round robin tests are often performed by oil companies in order to evaluate the repeatability and reproducibility of the methods used to control the quality of their products. It is very important to identify the laboratories that present statistically non-coherent results (outliers) in order to avoid an unjustified overestimation of the results variability. These round robin tests may involve more than 30 laboratories with an associated risk of more than two laboratories considered as outliers. We had presented in our paper « The Limitations of the Cochran and Grubbs Outlier Tests in Round Robin Testing », the classical statistical tests of outliers detection (Cochran and Grubbs' tests) described in the ISO normative documents used to analyze the round robin tests, their inefficiency in the situations of masking effect (1) and some simple new algorithms derived from the Fisher and Student statistics which give interesting results. In this presentation, they are compared now with robust approaches and in particular, with Huber's test.