Applying robust regression to insurance

A statistical procedure is called robust if it is insensitive to the occurence of gross errors in the data. The ordinary least squares regression technique does not satisfy this property, because even a single outlier can totally offset the result. Therefore, the least trimmed squares (LTS) technique is introduced, which can resist the effect of a large percentage of outliers. The latter method is illustrated on data concerning life insurance, pension funds, health insurance, and inflation.