Journal
JOURNAL OF STATISTICAL SOFTWARE
Volume 74, Issue 1, Pages 1-31Publisher
JOURNAL STATISTICAL SOFTWARE
DOI: 10.18637/jss.v074.i01
Keywords
additive models; prediction intervals; high-dimensional data
Funding
- Deutsche Forschungsgemeinschaft (DFG) [SCHM-2966/1-1]
- Interdisciplinary Center for Clinical Research (IZKF) of the Friedrich-Alexander University Erlangen-Nurnberg [J49]
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Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=gamboostLSS.
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