6 Outlier identification and robust methods

This chapter describes the behavior of an outlier identification procedure in the “average” and concludes that one-step and inward and outward testing procedures using robust location and scale estimators show a better behavior than their “classical" competitors.

Abstract

This chapter discusses the general principle of outlier generating models. It describes four main types of outlier identification rules—namely, block procedures, inward testing methods, outward testing methods, and one-step identification procedures. The chapter describes the behavior of an outlier identification procedure in the “average.” It focuses on the average proportion of correctly detected outliers. The results for labeled, Ferguson type and α outlier models are highlighted in the chapter. Summarizing such results, also based on further simulations with other outlier generating models, and in addition taking into account the findings in Davies and Gather, a conclusion is drawn that states that one-step and inward and outward testing procedures using robust location and scale estimators show a better behavior than their “classical” competitors.