Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit (see Chapter 6, Section 6.1, in the text). Another approach, termed robust regression, is to employ a fitting criterion that is not as vulnerable as least squares to unusual data.