ArticleStat2013
Variable Selection in Generalized Functional Linear Models.
Article in Stat, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- A novel high-dimensional model for identifying regional DNA methylation QTLs.Biostatistics (Oxford, England) · 2025Article
- High-dimensional inference for functional regression with an application to the Alzheimer's disease magnetoencephalography study.Biostatistics (Oxford, England) · 2025Article
- Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.Bayesian analysis · 2024Article
- Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease.Biostatistics (Oxford, England) · 2023Article
- Constructing a polygenic risk score for childhood obesity using functional data analysis.Econometrics and statistics · 2023Article
- Inference in Functional Linear Quantile Regression.Journal of multivariate analysis · 2022Article
- Understanding rice growth-promoting potential ofCurrent research in microbial sciences · 2021Article
- Covariate-adjusted region-referenced generalized functional linear model for EEG data.Statistics in medicine · 2019Article
- Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma Data.Journal of the American Statistical Association · 2019Article
- Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control.PloS one · 2019Article
- Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown.Journal of the Royal Statistical Society. Series C, Applied statistics · 2018Article
- Feature selection for high-dimensional temporal data.BMC bioinformatics · 2018Article
- Miscellanea Dependent generalized functional linear models.Biometrika · 2017Article
- Article
- Methods for scalar-on-function regression.International statistical review = Revue internationale de statistique · 2017Article
- A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.Statistics in medicine · 2017Article
- FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis.Bioinformatics (Oxford, England) · 2017Article
- Generalized Scalar-on-Image Regression Models via Total Variation.Journal of the American Statistical Association · 2017Article
- A Two Sample Distribution-Free Test for Functional Data with Application to a Diffusion Tensor Imaging Study of Multiple Sclerosis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2016Article
- Interaction Models for Functional Regression.Computational statistics & data analysis · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Modern research data, where a large number of functional predictors is collected on few subjects are becoming increasingly common. In this paper we propose a variable selection technique, when the predictors are functional and the response is scalar. Our approach is based on adopting a generalized functional linear model framework and using a penalized likelihood method that simultaneously controls the sparsity of the model and the smoothness of the corresponding coefficient functions by adequate penalization. The methodology is characterized by high predictive accuracy, and yields interpretable models, while retaining computational efficiency. The proposed method is investigated numerically in finite samples, and applied to a diffusion tensor imaging tractography data set and a chemometric data set.
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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.