ArticleJournal of machine learning research : JMLR2021
Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach.
Article in Journal of machine learning research : JMLR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Generalized linear multiple kernel learning for high-dimensional image data.Statistics & probability letters · 2027Article
- Inference for Deep Neural Network Estimators in Generalized Nonparametric Models.Journal of the American Statistical Association · 2026Article
- Statistical inference on high-dimensional covariate-dependent Gaussian graphical regressions.Biometrics · 2025Article
- Debiased lasso after sample splitting for estimation and inference in high-dimensional generalized linear models.The Canadian journal of statistics = Revue canadienne de statistique · 2025Article
- Metabolomic Biomarkers of Dietary Approaches to Stop Hypertension (DASH) Dietary Patterns in Pregnant Women.Nutrients · 2024Article
- Debiased lasso for stratified Cox models with application to the national kidney transplant data.The annals of applied statistics · 2023Article
- Statistical Inference for Cox Proportional Hazards Models with a Diverging Number of Covariates.Scandinavian journal of statistics, theory and applications · 2023Article
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- Online inference in high-dimensional generalized linear models with streaming data.Electronic journal of statistics · 2023Article
- A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models.Journal of machine learning research : JMLR · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The focus of modern biomedical studies has gradually shifted to explanation and estimation of joint effects of high dimensional predictors on disease risks. Quantifying uncertainty in these estimates may provide valuable insight into prevention strategies or treatment decisions for both patients and physicians. High dimensional inference, including confidence intervals and hypothesis testing, has sparked much interest. While much work has been done in the linear regression setting, there is lack of literature on inference for high dimensional generalized linear models. We propose a novel and computationally feasible method, which accommodates a variety of outcome types, including normal, binomial, and Poisson data. We use a "splitting and smoothing" approach, which splits samples into two parts, performs variable selection using one part and conducts partial regression with the other part. Averaging the estimates over multiple random splits, we obtain the smoothed estimates, which are numerically stable. We show that the estimates are consistent, asymptotically normal, and construct confidence intervals with proper coverage probabilities for all predictors. We examine the finite sample performance of our method by comparing it with the existing methods and applying it to analyze a lung cancer cohort study.
Indexed as
Identifiers
34531706PMC8442657What OpenQuestion holds
Registered trials
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.