ArticleProceedings of the National Academy of Sciences of the United States of America2019
A modern maximum-likelihood theory for high-dimensional logistic regression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
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- REFINE2: a simplified simulation tool to help epidemiologists evaluate the suitability and sensitivity of effect estimation within user-specified data.American journal of epidemiology · 2026Article
- Finite Sample Rates for Logistic Regression with Small Noise or Few Samples.Sankhya. Series A. (2008) · 2026Article
- Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.Statistics in medicine · 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
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- Depressive symptoms among rural left-behind children and adolescents in China: a large-scale cross-sectional study.BMC public health · 2024Article
- Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.Frontiers in human neuroscience · 2024Article
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- Approximate message passing from random initialization with applications toProceedings of the National Academy of Sciences of the United States of America · 2023Article
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- Debiased lasso for generalized linear models with a diverging number of covariates.Biometrics · 2023Article
- Online inference in high-dimensional generalized linear models with streaming data.Electronic journal of statistics · 2023Article
- Comments on "A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models".Journal of the American Statistical Association · 2023Article
- Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data.Journal of the American Statistical Association · 2023Article
- Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes.Journal of the American Statistical Association · 2023Article
- Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.Journal of personalized medicine · 2022Article
- Variational Disentanglement for Rare Event Modeling.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2021Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Students in statistics or data science usually learn early on that when the sample size n is large relative to the number of variables p, fitting a logistic model by the method of maximum likelihood produces estimates that are consistent and that there are well-known formulas that quantify the variability of these estimates which are used for the purpose of statistical inference. We are often told that these calculations are approximately valid if we have 5 to 10 observations per unknown parameter. This paper shows that this is far from the case, and consequently, inferences produced by common software packages are often unreliable. Consider a logistic model with independent features in which
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