Variable screening based on Gaussian Centered L-moments

An important challenge in big data is identification of important variables. For this purpose, methods of discovering variables with non-standard univariate marginal distributions are proposed. The conventional moments based summary statistics can be well-adopted, but their sensitivity to outliers can lead to selection based on a few outliers rather than distributional shape such as bimodality. To address this type of non-robustness, the L -moments are considered. Using these in practice, however, has a limitation since they do not take zero values at the Gaussian distributions to which the shape of a marginal distribution is most naturally compared. As a remedy, Gaussian Centered L-moments are proposed, which share advantages of the L-moments, but have zeros at the Gaussian distributions. The strength of Gaussian Centered L-moments over other conventional moments is shown in theoretical and practical aspects such as their performances in screening important genes in cancer genetics data.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Identification of important variables in big data is a crucial challenge. To tackle this, methods for discovering variables with non-standard univariate marginal distributions are proposed. Traditional moments-based summary statistics can be sensitive to outliers, thus L-moments are considered for robustness. However, the limitation of L-moments is addressed by proposing Gaussian Centered L-moments.