Robustness and Outlier Resistance

There is often a particular concern for the effects of outliers or heavy-tailed distributions when using standard statistical techniques. To address this type of a situation, a parametric approach would be to use ML estimation assuming a heavy-tailed distribution (perhaps a Student t distribution with few degrees of freedom). However, simple ad hoc methods such as trimmed means may be useful. There is a large statistical literature on robust and outlier-resistant methods, (e.g., Hoaglin et al. 1983 Barnett and Lewis 1994). 

As with many terms used in the section, the term robust is often used differently by statisticians, relative to use by other scientists. In statistical terminology, the term robust denotes that a procedure will perform well under different situations (not only if a single particular model is assumed to be true). Often the term refers to outlier resistance, particularly relative to methods that are optimal under normality assumptions. 

---