ArticleBiometrics2019
Drawing inferences for high-dimensional linear models: A selection-assisted partial regression and smoothing approach.
Article in Biometrics, 2019. 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.
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Who cites it
5 citing papers in PubMed.
- 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
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- Inference for High-Dimensional Censored Quantile Regression.Journal of the American Statistical Association · 2023Article
- Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach.Journal of machine learning research : JMLR · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Drawing inferences for high-dimensional models is challenging as regular asymptotic theories are not applicable. This article proposes a new framework of simultaneous estimation and inferences for high-dimensional linear models. By smoothing over partial regression estimates based on a given variable selection scheme, we reduce the problem to low-dimensional least squares estimations. The procedure, termed as Selection-assisted Partial Regression and Smoothing (SPARES), utilizes data splitting along with variable selection and partial regression. We show that the SPARES estimator is asymptotically unbiased and normal, and derive its variance via a nonparametric delta method. The utility of the procedure is evaluated under various simulation scenarios and via comparisons with the de-biased LASSO estimators, a major competitor. We apply the method to analyze two genomic datasets and obtain biologically meaningful results.
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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.