Robust statistics

Robust stat ist ics seeks to provide methods that emulate popular stat ist ical methods, but which are not unduly af fected by out liers or other small departures f rom model assumptions. In stat ist ics, classical est imat ion methods rely heavily on assumptions which are of ten not met in pract ice. In part icular, it is of ten assumed that the data errors are normally distributed, at least approximately, or that the central limit theorem can be relied on to produce normally distributed est imates. Unfortunately, when there are out liers in the data, classical est imators of ten have very poor performance, when judged using the breakdown point and the influence function, described below.