ArticleJournal of the American Statistical Association2019
Robust Variable and Interaction Selection for Logistic Regression and General Index Models.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Epidemiology of Overweight and Obesity in Early Childhood in China and Associated Factors.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Think and Choose! The Dual Impact of Label Information and Consumer Attitudes on the Choice of a Plant-Based Analog.Foods (Basel, Switzerland) · 2024Article
- Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.Nature communications · 2024Article
- Unified model-free interaction screening via CV-entropy filter.Computational statistics & data analysis · 2023Article
- The Kendall interaction filter for variable interaction screening in high dimensional classification problems.Journal of applied statistics · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- An easy-to-use nomogram predicting overall survival of adult acute lymphoblastic leukemia.Frontiers in oncology · 2022Article
- High Survivorship of First-Generation Monarch Butterfly Eggs to Third Instar Associated with a Diverse Arthropod Community.Insects · 2021Article
- IMMIGRATE: A Margin-Based Feature Selection Method with Interaction Terms.Entropy (Basel, Switzerland) · 2020Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Under the logistic regression framework, we propose a forward-backward method, SODA, for variable selection with both main and quadratic interaction terms. In the forward stage, SODA adds in predictors that have significant overall effects, whereas in the backward stage SODA removes unimportant terms to optimize the extended Bayesian Information Criterion (EBIC). Compared with existing methods for variable selection in quadratic discriminant analysis, SODA can deal with high-dimensional data in which the number of predictors is much larger than the sample size and does not require the joint normality assumption on predictors, leading to much enhanced robustness. We further extend SODA to conduct variable selection and model fitting for general index models. Compared with existing variable selection methods based on the Sliced Inverse Regression (SIR) (Li, 1991), SODA requires neither linearity nor constant variance condition and is thus more robust. Our theoretical analysis establishes the variable-selection consistency of SODA under high-dimensional settings, and our simulation studies as well as real-data applications demonstrate superior performances of SODA in dealing with non-Gaussian design matrices in both logistic and general index models.
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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.