Estimating the local false discovery rate via a bootstrap solution to the reference class problem.

ABSTRACT: Methods of estimating the local false discovery rate (LFDR) have been applied to different types of datasets such as high-throughput biological data, diffusion tensor imaging (DTI), and genome-wide association (GWA) studies. We present a model for LFDR estimation that incorporates a covariate into each test. Incorporating the covariates may improve the performance of testing procedures, because it contains additional information based on the biological context of the corresponding test. This method provides different estimates depending on a tuning parameter. We estimate the optimal value of that parameter by choosing the one that minimizes the estimated LFDR resulting from the bias and variance in a bootstrap approach. This estimation method is called an adaptive reference class (ARC) met

SUBMITTER: Abbas-Aghababazadeh F 

PROVIDER: S-EPMC6261018 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature