A significance test for graph-constrained estimation.

ABSTRACT: Graph-constrained estimation methods encourage similarities among neighboring covariates presented as nodes of a graph, and can result in more accurate estimates, especially in high-dimensional settings. Variable selection approaches can then be utilized to select a subset of variables that are associated with the response. However, existing procedures do not provide measures of uncertainty of estimates. Further, the vast majority of existing approaches assume that available graph accurately captures the association among covariates; violations to this assumption could severely hurt the reliability of the resulting estimates. In this article, we present a new inference framework, called the Grace test, which produces coefficient estimates and corresponding p-values by incorporating the ext

SUBMITTER: Zhao S 

PROVIDER: S-EPMC4828333 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature