595 research outputs found
Boosting Additive Models using Component-wise P-Splines
We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous from a computational point of view. In particular, we give a detailed analysis on the effect of various P-spline hyper-parameters on the boosting fit. In addition, we derive a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates
Variable Selection and Model Choice in Structured Survival Models
In many situations, medical applications ask for flexible survival models that allow to extend the classical Cox-model via the
inclusion of time-varying and nonparametric effects. These structured survival models are very flexible but additional
difficulties arise when model choice and variable selection is desired. In particular, it has to be decided which covariates
should be assigned time-varying effects or whether parametric modeling is sufficient for a given covariate. Component-wise
boosting provides a means of likelihood-based model fitting that enables simultaneous variable selection and model choice. We
introduce a component-wise likelihood-based boosting algorithm for survival data that permits the inclusion of both parametric
and nonparametric time-varying effects as well as nonparametric effects of continuous covariates utilizing penalized splines as
the main modeling technique. Its properties
and performance are investigated in simulation studies.
The new modeling approach is used to build a flexible survival model for
intensive care patients suffering from severe sepsis.
A software implementation is available to the interested reader
Boosting Functional Response Models for Location, Scale and Shape with an Application to Bacterial Competition
We extend Generalized Additive Models for Location, Scale, and Shape (GAMLSS)
to regression with functional response. This allows us to simultaneously model
point-wise mean curves, variances and other distributional parameters of the
response in dependence of various scalar and functional covariate effects. In
addition, the scope of distributions is extended beyond exponential families.
The model is fitted via gradient boosting, which offers inherent model
selection and is shown to be suitable for both complex model structures and
highly auto-correlated response curves. This enables us to analyze bacterial
growth in \textit{Escherichia coli} in a complex interaction scenario,
fruitfully extending usual growth models.Comment: bootstrap confidence interval type uncertainty bounds added; minor
changes in formulation
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