ArticleJournal of machine learning research : JMLR2021
Inference for the Case Probability in High-dimensional Logistic Regression.
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 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Adaptive Debiased Lasso in High-Dimensional Generalized Linear Models with Streaming Data.Journal of the American Statistical Association · 2026Article
- Inference for Deep Neural Network Estimators in Generalized Nonparametric Models.Journal of the American Statistical Association · 2026Article
- Article
- Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes.Journal of the American Statistical Association · 2023Article
- Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction.Journal of machine learning research : JMLRArticle
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Authors and funding
4 authors.
Funding
Abstract
Labeling patients in electronic health records with respect to their statuses of having a disease or condition, i.e. case or control statuses, has increasingly relied on prediction models using high-dimensional variables derived from structured and unstructured electronic health record data. A major hurdle currently is a lack of valid statistical inference methods for the case probability. In this paper, considering high-dimensional sparse logistic regression models for prediction, we propose a novel bias-corrected estimator for the case probability through the development of linearization and variance enhancement techniques. We establish asymptotic normality of the proposed estimator for any loading vector in high dimensions. We construct a confidence interval for the case probability and propose a hypothesis testing procedure for patient case-control labelling. We demonstrate the proposed method via extensive simulation studies and application to real-world electronic health record data.
Indexed as
Identifiers
35935001PMC9354733What 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.