Interpretation of regression coefficients is sensitive to the scale of the inputs. One method often used to place input variables on a common scale is to divide each numeric variable by its standard deviation. Here we propose dividing each numeric variable by two times its standard deviation, so that the generic comparison is with inputs equal to the mean +/-1 standard deviation. The resulting ...

A new graphical procedure for multiple comparisons of k standard deviations is provided. As a test for homogeneity of variances, the new procedure has similar type I and type II error properties as the Brown and Forsythe (1974) version of the Levene (1960) test, W50. The graphical display associated with the multiple comparisons test, however, provides a useful visual tool for screening samples...

In this paper, the high-dimensional sparse linear regression model is considered, where the overall number of variables is larger than the number of observations. We investigate the L1 penalized least absolute deviation method. Different from most of other methods, the L1 penalized LAD method does not need any knowledge of standard deviation of the noises or any moment assumptions of the noises...