Linear or smooth? Enhanced model choice in boosting via deselection of base-learners

Mayr A, Wistuba T, Speller J, Gude F, Hofner B (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Statistical Modelling SAGE Publications (UK and US)

DOI: 10.1177/1471082X231170045

Abstract

The specification of a particular type of effect (e.g., linear or non-linear) of a covariate in a regression model can be either based on graphical assessment, subject matter knowledge or also on data-driven model choice procedures. For the latter variant, we present a boosting approach that is available for a huge number of different model classes. Boosting is an indirect regularization technique that leads to variable selection and can easily incorporate also non-linear or smooth effects. Furthermore, the algorithm can be adapted in a way to automatically select whether to model a continuous variable with a smooth or a linear effect. We enhance this model choice procedure by trying to compensate the inherent bias towards the more complex effect by incorporating a pragmatic and simple deselection technique that was originally implemented for enhanced variable selection. We illustrate our approach in the analysis of T3 thyroid hormone levels from a larger Galician cohort and investigate its performance in a simulation study.

Authors with CRIS profile

Benjamin Hofner

Involved external institutions

Rheinische Friedrich-Wilhelms-Universität Bonn DE Germany (DE) Instituto de Investigación Sanitaria de Santiago de Compostela ES Spain (ES) Paul Ehrlich Institute / Paul-Ehrlich-Institut – Bundesinstitut für Impfstoffe und biomedizinische Arzneimittel (PEI) DE Germany (DE)

How to cite

APA:

Mayr, A., Wistuba, T., Speller, J., Gude, F., & Hofner, B. (2023). Linear or smooth? Enhanced model choice in boosting via deselection of base-learners. Statistical Modelling. https://dx.doi.org/10.1177/1471082X231170045

MLA:

Mayr, Andreas, et al. "Linear or smooth? Enhanced model choice in boosting via deselection of base-learners." Statistical Modelling (2023).

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