Generalized additive models for location, scale and shape for high dimensional data -- a flexible approach based on boosting

DOI10.1111/j.1467-9876.2011.01033.xMaRDI QIDQ6637654

Nora Fenske, Matthias Schmid, Thomas Kneib, Benjamin Hofner, Andreas Mayr

Publication date: 13 November 2024

Published in: Journal of the Royal Statistical Society. Series C. Applied Statistics (Search for Journal in Brave)

zbMATH Keywords

variable selectionhigh dimensional datagradient boostingspatial informationscale and shapegeneralized additive models for locationprediction inference

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items (8)

Distribution-Free Location-Scale Regression ⋮ Model averaging estimation for nonparametric varying-coefficient models with multiplicative heteroscedasticity ⋮ Semi-Structured Distributional Regression ⋮ Boosting multivariate structured additive distributional regression models ⋮ Gradient boosting for linear mixed models ⋮ Robust statistical boosting with quantile-based adaptive loss functions ⋮ Significance tests for boosted location and scale models with linear base-learners ⋮ Linear or smooth? Enhanced model choice in boosting via deselection of base-learners

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