On data-driven choice of \(\lambda\) in nonparametric Gaussian regression via propagation-separation approach

DOI10.1016/j.csda.2020.107087OpenAlexW3084211423MaRDI QIDQ829716

Ewelina Marta Fiebig

Publication date: 6 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2020.107087

zbMATH Keywords

cross-validationnonparametric estimationadaptive weights smoothingGaussian regressionpropagation-separation

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• AWS
• KernSmooth
• adimpro

Cites Work

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• A different perspective on the propagation-separation approach
• Identifying Berlin's land value map using adaptive weights smoothing
• Nonparametric and semiparametric models.
• Propagation-separation approach for local likelihood estimation
• Structural Adaptive Smoothing: Principles and Applications in Imaging
• On Non-Parametric Estimates of Density Functions and Regression Curves

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