Fusion learning of functional linear regression with application to genotype-by-environment interaction studies

DOI10.1007/s13253-023-00529-2OpenAlexW4319297246MaRDI QIDQ6050916

Dan Nettleton, Aaron M. Kusmec, Li Wang, Shan Yu

Publication date: 12 October 2023

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-023-00529-2

zbMATH Keywords

spline approximationheterogeneitytime-varying coefficientgraph-constrained Lasso

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Variable selection and regression analysis for graph-structured covariates with an application to genomics
• Estimation and variable selection for generalized additive partial linear models
• Cluster analysis of longitudinal profiles with subgroups
• Continuous time-varying Kriging for spatial prediction of functional data: an environmental application
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Estimating Truncated Functional Linear Models With a Nested Group Bridge Approach
• Adaptive nonparametric regression with the K-nearest neighbour fused lasso
• iFusion: Individualized Fusion Learning
• Estimation and Inference for Generalized Geoadditive Models
• TIME-VARYING ESTIMATION AND DYNAMIC MODEL SELECTION WITH AN APPLICATION OF NETWORK DATA
• Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets

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