LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape
Andreas Groll (),
Julien Hambuckers,
Thomas Kneib () and
Nikolaus Umlauf ()
Working Papers from Faculty of Economics and Statistics, Universität Innsbruck
Abstract:
For numerous applications it is of interest to provide full probabilistic forecasts, which are able to assign probabilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic distributional models capturing location, scale, shape (and other aspects) of the response distribution. One of the most established models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high dimensional data set-ups classical fitting procedures for the GAMLSS often become rather unstable and methods for variable selection are desirable. Therefore, we propose a regularization approach for high dimensional data set-ups in the framework for GAMLSS. It is designed for linear covariate effects and is based on L1 -type penalties. The following three penalization options are provided: the conventional least absolute shrinkage and selection operator (LASSO) for metric covariates, and both group and fused LASSO for categorical predictors. The methods are investigated both for simulated data and for two real data examples, namely Munich rent data and data on extreme operational losses from the Italian bank UniCredit.
Keywords: GAMLSS; distributional regression; model selection; LASSO; fused LASSO (search for similar items in EconPapers)
JEL-codes: C13 C15 C18 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2018-08
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (1)
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Related works:
Journal Article: LASSO-type penalization in the framework of generalized additive models for location, scale and shape (2019) 
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Persistent link: https://EconPapers.repec.org/RePEc:inn:wpaper:2018-16
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