Model-based boosting in R: a hands-on tutorial using the R package mboost

Hofner, Benjamin; Mayr, Andreas; Robinzonov, Nikolay; Schmid, Matthias

doi:10.1007/s00180-012-0382-5

Cited by 213 publications

(268 citation statements)

References 30 publications

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“…Higher numbers of m stop hence lead to larger, more complex models while smaller numbers lead to sparser models with less complexity. In practice, m stop is often selected via cross-validation or resampling methods, by selecting the value that leads to the smallest empirical risk on test data (Hofner et al, 2014). For theoretical insights on the general concept of boosting algorithms, we refer to the work of Zhang and Yu (2005) who studied the numerical convergence and consistency with different loss functions.…”

Section: Component-wise Gradient Boostingmentioning

confidence: 99%

“…However, in practice, there exists a quasi-linear relation between the step-length and the needed number of boosting iterations (Schmid and Hothorn, 2008). As a result, it is often recommended to use a fixed small value of ν = 0.1 for the step-length and optimize the stopping iteration instead Hofner et al, 2014). In case of boosting JM, where two additive predictors are fitted and potentially two boosting updates are carried out in each iteration of the algorithms, it is hard to justify why both predictors should be optimal after the same number of boosting iterations (i.e.…”

Section: Boosting Joint Modelsmentioning

confidence: 99%

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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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Section: Component-wise Gradient Boostingmentioning

confidence: 99%

Section: Boosting Joint Modelsmentioning

confidence: 99%

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

et al. 2017

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“…To make this tutorial self-contained, we try to shortly explain all relevant features here as well. However, a dedicated hands-on tutorial is available for an applied introduction to mboost (Hofner, Mayr, Robinzonov, and Schmid 2014).…”

Section: The Package Gamboostlssmentioning

confidence: 99%

“…The choice of base-learners is crucial for the application of the gamboostLSS algorithm, as they define the type(s) of effect(s) that covariates will have on the predictors of the GAMLSS distribution parameters. See Hofner et al (2014) for details and application notes on the base-learners.…”

Section: Base-learnersmentioning

confidence: 99%

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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“…The one to be updated is chosen by evaluating fits to the gradient, the resulting fits will indicate an element which improves the overall fit the most. And this also lead to the sparseness of the solutions [15], since many coefficients will be estimated to zeros.…”

Section: B Boosting As Functional Gradient Decentmentioning

confidence: 99%