4.7 Article

High-Dimensional Regression Adjustment Estimation for Average Treatment Effect with Highly Correlated Covariates

Related references

Note: Only part of the references are listed.
Article Biochemical Research Methods

A variable selection approach for highly correlated predictors in high-dimensional genomic data

Wencan Zhu et al.

Summary: The novel variable selection approach WLasso takes into account correlations between biomarkers and performs well in high-dimensional linear models. Results show that WLasso outperforms other methods in scenarios with highly correlated biomarkers.

BIOINFORMATICS (2021)

Article Computer Science, Interdisciplinary Applications

Regression adjustment for treatment effect with multicollinearity in high dimensions

Lili Yue et al.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2019)

Article Statistics & Probability

ARE DISCOVERIES SPURIOUS? DISTRIBUTIONS OF MAXIMUM SPURIOUS CORRELATIONS AND THEIR APPLICATIONS

Jianqing Fan et al.

ANNALS OF STATISTICS (2018)

Article Biology

Robust estimation of high-dimensional covariance and precision matrices

Marco Avella-Medina et al.

BIOMETRIKA (2018)

Article Mathematical & Computational Biology

A framework for estimating and testing qualitative interactions with applications to predictive biomarkers

Jeremy Roth et al.

BIOSTATISTICS (2018)

Article Economics

PROGRAM EVALUATION AND CAUSAL INFERENCE WITH HIGH-DIMENSIONAL DATA

A. Belloni et al.

ECONOMETRICA (2017)

Article Multidisciplinary Sciences

High-dimensional regression adjustments in randomized experiments

Stefan Wager et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)

Article Multidisciplinary Sciences

Lasso adjustments of treatment effect estimates in randomized experiments

Adam Bloniarz et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)

Article Statistics & Probability

On estimation of the diagonal elements of a sparse precision matrix

Samuel Balmand

ELECTRONIC JOURNAL OF STATISTICS (2016)

Article Computer Science, Information Systems

Oracle Inequalities for a Group Lasso Procedure Applied to Generalized Linear Models in High Dimension

Melanie Blazere et al.

IEEE TRANSACTIONS ON INFORMATION THEORY (2014)

Article Economics

Inference on Treatment Effects after Selection among High-Dimensional ControlsaEuro

Alexandre Belloni et al.

REVIEW OF ECONOMIC STUDIES (2014)

Article Statistics & Probability

AGNOSTIC NOTES ON REGRESSION ADJUSTMENTS TO EXPERIMENTAL DATA: REEXAMINING FREEDMAN'S CRITIQUE

Winston Lin

ANNALS OF APPLIED STATISTICS (2013)

Article Statistics & Probability

A Constrained l1 Minimization Approach to Sparse Precision Matrix Estimation

Tony Cai et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2011)

Article Statistics & Probability

ON REGRESSION ADJUSTMENTS IN EXPERIMENTS WITH SEVERAL TREATMENTS

David A. Freedman

ANNALS OF APPLIED STATISTICS (2008)

Article Statistics & Probability

Sure independence screening for ultrahigh dimensional feature space

Jianqing Fan et al.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2008)

Article Statistics & Probability

Regularization and variable selection via the elastic net

H Zou et al.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2005)

Article Economics

Nonparametric estimation of average treatment effects under exogeneity: A review

GW Imbens

REVIEW OF ECONOMICS AND STATISTICS (2004)

Article Statistics & Probability

Nonconcave penalized likelihood with a diverging number of parameters

JQ Fan et al.

ANNALS OF STATISTICS (2004)

Article Statistics & Probability

Comparison of discrimination methods for the classification of tumors using gene expression data

S Dudoit et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2002)

Article Statistics & Probability

Variable selection via nonconcave penalized likelihood and its oracle properties

JQ Fan et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2001)