ArticleComputational statistics & data analysis2018
A Forward and Backward Stagewise Algorithm for Nonconvex Loss Functions with Adaptive Lasso.
Article in Computational statistics & data analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Integrative analysis of high-dimensional quantile regression with contrasted penalization.Journal of applied statistics · 2025Article
- A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Default risk prediction and feature extraction using a penalized deep neural network.Statistics and computing · 2022Article
- Marginal false discovery rate for a penalized transformation survival model.Computational statistics & data analysis · 2021Article
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4 authors.
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Abstract
Penalization is a popular tool for multi- and high-dimensional data. Most of the existing computational algorithms have been developed for convex loss functions. Nonconvex loss functions can sometimes generate more robust results and have important applications. Motivated by the BLasso algorithm, this study develops the Forward and Backward Stagewise (Fabs) algorithm for nonconvex loss functions with the adaptive Lasso (aLasso) penalty. It is shown that each point along the Fabs paths is a
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