Robust Transformations in Univariate and Multivariate Time Series
提出一种基于稳健得分检验统计量的方法，用于选择不受异常观测影响的变换，以改善误差的同方差性和近似正态性。
It is well known that transformation of the response may improve the homogeneity and the approximate normality of the errors. Unfortunately, the estimated transformation and related test statistic may be sensitive to the presence of one, or several, atypical observations. In addition, it is important to remark that outliers in one transformed scale may not be atypical in another scale. Therefore, it is important to choose a transformation which does not depend on the presence of particular observations. In this article we suggest an efficient procedure based on a robust score test statistic which quantifies the effect of each observation on the choice of the transformation.