ArticleEntropy (Basel, Switzerland)2020
Consistent Estimation of Generalized Linear Models with High Dimensional Predictors via Stepwise Regression.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Machine learning-based prediction of preeclampsia using first-trimester inflammatory markers and red blood cell indices.BMC pregnancy and childbirth · 2025Article
- Article
- Quantile forward regression for high-dimensional survival data.Lifetime data analysis · 2023Article
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries.Frontiers in pediatrics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Predictive models play a central role in decision making. Penalized regression approaches, such as least absolute shrinkage and selection operator (LASSO), have been widely used to construct predictive models and explain the impacts of the selected predictors, but the estimates are typically biased. Moreover, when data are ultrahigh-dimensional, penalized regression is usable only after applying variable screening methods to downsize variables. We propose a stepwise procedure for fitting generalized linear models with ultrahigh dimensional predictors. Our procedure can provide a final model; control both false negatives and false positives; and yield consistent estimates, which are useful to gauge the actual effect size of risk factors. Simulations and applications to two clinical studies verify the utility of the method.
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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.