There are many statistics which can be used to characterize data sets and provide valuable information regarding the data distribution, even for large samples. Traditional measures, such as skewness and kurtosis, mentioned in introductory statistics courses, are rarely applied. A variety of other measures of tail length, skewness and tail weight have been proposed, which can be used to describe the underlying population distribution. Adaptive statistical procedures change the estimator of location, depending on sample characteristics. The success of these estimators depends on correctly classifying the underlying distribution model. Advocates of adaptive distribution testing propose to proceed by assuming (1) that an appropriate model, say Omega , is such that Omega { Omega , Omega , i i 1 2 … , Omega }, and (2) that the character of the model selection process is statistically k independent of the hypothesis testing. We review the development of adaptive linear estimators and adaptive maximum-likelihood estimators.
