Generalized Additive Models for Location, Scale and Shape for High Dimensional Data—A Flexible Approach Based on Boosting

Mayr, Andreas; Fenske, Nora; Hofner, Benjamin; Kneib, Thomas; Schmid, Matthias

doi:10.1111/j.1467-9876.2011.01033.x

Cited by 135 publications

(220 citation statements)

References 35 publications

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“…gamboostLSS is an algorithm to fit GAMLSS models via component-wise gradient boosting (Mayr et al 2012a) adapting an earlier strategy by Schmid, Potapov, Pfahlberg, and Hothorn (2010). While the concept of boosting emerged from the field of supervised machine learning, boosting algorithms are nowadays often applied as a flexible alternative to estimate and select predictor effects in statistical regression models (statistical boosting; Mayr, Binder, Gefeller, and Schmid 2014).…”

Section: Boosting Gamlss Modelsmentioning

confidence: 99%

“…A discussion of model comparison methods and diagnostic checks can be found in Section 5.4. The complete gamboostLSS algorithm can be found in Appendix A and is described in detail in Mayr et al (2012a).…”

Section: Boosting Gamlss Modelsmentioning

confidence: 99%

“…An evaluation of computing times for up to p = 10000 predictors can be found in Binder et al (2012). In case of boosting GAMLSS, the computational complexity additionally increases with the number of distribution parameters K. For an example on the performance of gamboostLSS in case of p > n see the simulation studies provided in Mayr et al (2012a). To speed up computations for the tuning of the algorithm via crossvalidation or resampling, gamboostLSS incorporates parallel computing (see Section 5.5).…”

Section: Boosting Gamlss Modelsmentioning

confidence: 99%

“…Specifically, gamboostLSS implements the gamboostLSS algorithm, which is a new fitting method for GAMLSS that was recently introduced by Mayr, Fenske, Hofner, Kneib, and Schmid (2012a). The gamboostLSS algorithm uses the same optimization criterion as the Gauss-Newton type algorithms implemented in the package gamlss (namely, the log-likelihood of the model under consideration) and hence fits the same type of statistical model.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to gamlss, however, the gamboostLSS package operates within the component-wise gradient boosting framework for model fitting and variable selection (Bühlmann and Yu 2003;Bühlmann and Hothorn 2007). As demonstrated in Mayr et al (2012a), replacing Gauss-Newton optimization by boosting techniques leads to a considerable increase in flexibility: Apart from being able to fit basically any type of GAMLSS, gamboostLSS implements an efficient mechanism for variable selection and model choice. As a consequence, gamboostLSS is a convenient alternative to the AIC-based variable selection methods implemented in gamlss.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Hofner¹,

Mayr²,

Schmid³

2016

J. Stat. Soft.

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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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Section: Boosting Gamlss Modelsmentioning

confidence: 99%

Section: Boosting Gamlss Modelsmentioning

confidence: 99%

Section: Boosting Gamlss Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Hofner¹,

Mayr²,

Schmid³

2016

J. Stat. Soft.

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Boosting joint models for longitudinal and time‐to‐event data

et al. 2017

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Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modelling. Commonly, joint models are estimated in likelihood based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and which do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyse the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry which collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models.

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Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Smith

Hofner

Lamb

et al. 2019

Ecology and Evolution

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Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero‐inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey‐based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence‐absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.

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Generalized Additive Models for Location, Scale and Shape for High Dimensional Data—A Flexible Approach Based on Boosting

Cited by 135 publications

References 35 publications

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Boosting joint models for longitudinal and time‐to‐event data

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

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