Lasso-type penalization in the framework of generalized additive models for location, scale and shape

DOI10.1016/j.csda.2019.06.005zbMath1496.62119OpenAlexW2889845402WikidataQ127674949 ScholiaQ127674949MaRDI QIDQ2337322

Julien Hambuckers, Andreas Groll, Thomas Kneib, Nikolaus Umlauf

Publication date: 19 November 2019

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

Full work available at URL: https://orbi.uliege.be/handle/2268/236909

zbMATH Keywords

model selectionLassofused LassoGAMLSSdistributional regression

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (5)

An extreme value Bayesian Lasso for the conditional left and right tails ⋮ Testing a parameter restriction on the boundary for the g-and-h distribution: a simulated approach ⋮ Introducing Lasso-type penalisation to generalised joint regression modelling for count data ⋮ Variable Selection Using a Smooth Information Criterion for Distributional Regression Models ⋮ Robust fitting for generalized additive models for location, scale and shape

Uses Software

• R
• GAMLSS
• CasANOVA
• gamboostLSS
• bamlss

Cites Work

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• On the ``degrees of freedom of the lasso
• Simultaneous Factor Selection and Collapsing Levels in ANOVA
• The Group Lasso for Logistic Regression
• Probabilistic Forecasts, Calibration and Sharpness
• Model Selection and Estimation in Regression with Grouped Variables
• Generalized Additive Models for Location, Scale and Shape

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