ArticleJournal of the American Statistical Association
Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics.
Article in Journal of the American Statistical Association. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data.Journal of the American Statistical Association · 2023Article
- Estimating causal effects with hidden confounding using instrumental variables and environments.Electronic journal of statistics · 2023Article
- Negative binomial factor regression with application to microbiome data analysis.Statistics in medicine · 2022Article
- DOUBLY DEBIASED LASSO: HIGH-DIMENSIONAL INFERENCE UNDER HIDDEN CONFOUNDING.Annals of statistics · 2022Article
- An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting.Biostatistics (Oxford, England) · 2022Article
- Statistical methods for Mendelian randomization in genome-wide association studies: A review.Computational and structural biotechnology journal · 2022Review
- Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Disease.Journal of the American Statistical Association · 2022Article
- Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer.Cancer communications (London, England) · 2021Article
- Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data.NeuroImage · 2020Article
- Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data.Frontiers in genetics · 2020Article
- Article
- A directed learning strategy integrating multiple omic data improves genomic prediction.Plant biotechnology journal · 2019Article
- An integrative U method for joint analysis of multi-level omic data.BMC genetics · 2019Article
- On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments.Journal of the American Statistical Association · 2019Article
- Differential Analysis of Directed Networks.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2018Article
- A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.Journal of machine learning research : JMLR · 2018Article
- 2D association and integrative omics analysis in rice provides systems biology view in trait analysis.Communications biology · 2018Article
- On the use of kernel machines for Mendelian randomization.Quantitative biology (Beijing, China) · 2017Article
- Outcome-adaptive lasso: Variable selection for causal inference.Biometrics · 2017Article
- Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study.Statistical applications in genetics and molecular biology · 2017Article
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3 authors.
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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 sparsity-inducing penalty functions in both stages. The resulting procedure is efficiently implemented by coordinate descent optimization. For the representative
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.