publication . Other literature type . Research . 2010

GAMLSS for high-dimensional data - a flexible approach based on boosting

Mayr, Andreas; Fenske, Nora; Hofner, Benjamin; Kneib, Thomas; Schmid, Matthias;
Open Access English
  • Published: 13 Dec 2010
  • Publisher: Universitätsbibliothek der Ludwig-Maximilians-Universität München
  • Country: Germany
Abstract
Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high-dimensional GAMLSS that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of pred...
Subjects
free text keywords: Technische Reports, GAMLSS, high-dimensional data, gradient boosting, variable selection, prediction inference, spatial information, 510, ddc:510
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