fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

Abstract Background Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alternative to parametric methods, their prac...

Full description

Bibliographic Details
Main Authors: Jiaxing Lin, Alexander Sibley, Ivo Shterev, Andrew Nixon, Federico Innocenti, Cliburn Chan, Kouros Owzar
Format: Article
Language:English
Published: BMC 2019-06-01
Series:BMC Bioinformatics
Subjects:
Jonckheere-Terpstra
Linear rank statistic
Constrained inference
Robust statistic
Logarithmic complexity
Genome-wide association studies
Online Access:http://link.springer.com/article/10.1186/s12859-019-2869-3

Internet

http://link.springer.com/article/10.1186/s12859-019-2869-3

Similar Items