When is a feature really there: the SiZer approach

Statistical smoothing methods are useful for finding important and nonobvious structure in data. However, some of the features discovered in this way can be spurious sampling artifacts. The SiZer approach (based on studying statistical SIgnificance of ZERo crossings of smoothed estimates) to analyzing which visible features represent important underlying structures, is discussed.