Article (Scientific journals)
LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape
Groll, Andreas; Hambuckers, Julien; Kneib, Thomas et al.
2019In Computational Statistics and Data Analysis, 140, p. 59-74
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Keywords :
GAMLSS; LASSO; Distributional regression; model selection; fused LASSO
Abstract :
[en] For numerous applications, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities 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 GAMLSS often become rather unstable and methods for variable selection are desirable. Therefore, a regularization approach for high-dimensional data set-ups in the framework of GAMLSS is proposed. 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.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Groll, Andreas
Hambuckers, Julien ;  Université de Liège - ULiège > HEC Liège : UER > Finance de Marché
Kneib, Thomas
Umlauf, Nicholaus
Language :
English
Title :
LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape
Publication date :
December 2019
Journal title :
Computational Statistics and Data Analysis
ISSN :
0167-9473
eISSN :
1872-7352
Publisher :
Elsevier, Netherlands
Volume :
140
Pages :
59-74
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 17 June 2019

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