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

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.
Year of publication:
2018
Authors: Groll, Andreas ; Hambuckers, Julien ; Kneib, Thomas ; Umlauf, Nikolaus
Publisher: Innsbruck : University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
Subject: GAMLSS | distributional regression | model selection | LASSO | fused LASSO
Series:
Type of publication: Book / Working Paper
Type of publication (narrower categories): Working Paper
Language: English
Other identifiers:
1030868638 [GVK]
hdl:10419/184994 [Handle]
Classification: C13 - Estimation ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; c18
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011930753
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