Hypothesis Testing in High-Dimensional Instrumental Variables Regression With an Application to Genomics Data

DOI10.5705/ss.202019.0408zbMath1483.62180arXiv1910.09628OpenAlexW2981467523WikidataQ108863860 ScholiaQ108863860MaRDI QIDQ5067438

Jiarui Lu, Hongzhe Li

Publication date: 1 April 2022

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.09628

zbMATH Keywords

multiple testinginverse regressionFDR controldebiased estimationgenetical genomics

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15) Protein sequences, DNA sequences (92D20) Paired and multiple comparisons; multiple testing (62J15)

Uses Software

• glmnet

Cites Work

• Unnamed Item
• Testing Endogeneity with High Dimensional Covariates
• Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score
• Gaussian graphical model estimation with false discovery rate control
• Instrumental variables: an econometrician's perspective
• Estimation in high-dimensional linear models with deterministic design matrices
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• Simultaneous analysis of Lasso and Dantzig selector
• Endogeneity in high dimensions
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Scaled sparse linear regression
• Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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