Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics.

ABSTRACT: In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high dimensionality and unknown optimal instruments, we propose a two-stage regularization framework for identifying and estimating important covariate effects while selecting and estimating optimal instruments. The methodology extends the classical two-stage least squares estimator to high dimensions by exploiting sparsity using sparsit

SUBMITTER: Lin W 

PROVIDER: S-EPMC4573639 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature