TY - DATA T1 - Detection of non-Gaussianity PY - 2015/10/08 AU - Teresa Ledwina AU - Grzegorz Wyłupek UR - https://tandf.figshare.com/articles/journal_contribution/Detection_of_non_Gaussianity/1250828 DO - 10.6084/m9.figshare.1250828.v3 L4 - https://ndownloader.figshare.com/files/1805870 KW - sum KW - normality KW - variant KW - component KW - hypothesis KW - novel selection rules N2 - We develop two tests sensitive to various departures from composite goodness-of-fit hypothesis of normality. The tests are based on the sums of squares of some components naturally arising in decomposition of the Shapiro–Wilk-type statistic. Each component itself has diagnostic properties. The numbers of squared components in sums are determined via some novel selection rules based on the data. The new solutions prove to be effective tools in detecting a broad spectrum of sources of non-Gaussianity. We also discuss two variants of the new tests adjusted to verification of simple goodness-of-fit hypothesis of normality. These variants also compare well to popular competitors. ER -