A Model-free Variable Screening Method Based on Leverage Score.

ABSTRACT: With rapid advances in information technology, massive datasets are collected in all fields of science, such as biology, chemistry, and social science. Useful or meaningful information is extracted from these data often through statistical learning or model fitting. In massive datasets, both sample size and number of predictors can be large, in which case conventional methods face computational challenges. Recently, an innovative and effective sampling scheme based on leverage scores via singular value decompositions has been proposed to select rows of a design matrix as a surrogate of the full data in linear regression. Analogously, variable screening can be viewed as selecting rows of the design matrix. However, effective variable selection along this line of thinking remains elusive. In

SUBMITTER: Zhong W 

PROVIDER: S-EPMC10281711 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature