ArticleBiometrics2016
A significance test for graph-constrained estimation.
Article in Biometrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Doubly regularized generalized linear models for spatial observations with high-dimensional covariates.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- On the Use of Minimum Penalties in Statistical Learning.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.The annals of applied statistics · 2023Article
- NETWORK DIFFERENTIAL CONNECTIVITY ANALYSIS.The annals of applied statistics · 2022Article
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- In Defense of the Indefensible: A Very Naïve Approach to High-Dimensional Inference.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Connectivity-informed adaptive regularization for generalized outcomes.The Canadian journal of statistics = Revue canadienne de statistique · 2021Article
- Graph-based sparse linear discriminant analysis for high-dimensional classification.Journal of multivariate analysis · 2019Article
- Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data.Scientific reports · 2019Article
- A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Frontiers in genetics · 2019Review
- Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data.Statistics in biosciences · 2018Article
- KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA.The annals of applied statistics · 2018Article
- Joint Estimation of Precision Matrices in Heterogeneous Populations.Electronic journal of statistics · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Graph-constrained estimation methods encourage similarities among neighboring covariates presented as nodes of a graph, and can result in more accurate estimates, especially in high-dimensional settings. Variable selection approaches can then be utilized to select a subset of variables that are associated with the response. However, existing procedures do not provide measures of uncertainty of estimates. Further, the vast majority of existing approaches assume that available graph accurately captures the association among covariates; violations to this assumption could severely hurt the reliability of the resulting estimates. In this article, we present a new inference framework, called the Grace test, which produces coefficient estimates and corresponding p-values by incorporating the external graph information. We show, both theoretically and via numerical studies, that the proposed method asymptotically controls the type-I error rate regardless of the choice of the graph. We also show that when the underlying graph is informative, the Grace test is asymptotically more powerful than similar tests that ignore the external information. We study the power properties of the proposed test when the graph is not fully informative and develop a more powerful Grace-ridge test for such settings. Our numerical studies show that as long as the graph is reasonably informative, the proposed inference procedures deliver improved statistical power over existing methods that ignore external information.
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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.