Significance Tests for Boosted Location and Scale Models with Linear Base-Learners

Hepp Tobias (), Schmid Matthias and Mayr Andreas
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Hepp Tobias: Institut für medizinische Biometrie, Informatik und Epidemiologie, Medizinische Fakultät, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Schmid Matthias: Institut für medizinische Biometrie, Informatik und Epidemiologie, Medizinische Fakultät, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Mayr Andreas: Institut für medizinische Biometrie, Informatik und Epidemiologie, Medizinische Fakultät, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

The International Journal of Biostatistics, 2019, vol. 15, issue 1, 13

Abstract: Generalized additive models for location scale and shape (GAMLSS) offer very flexible solutions to a wide range of statistical analysis problems, but can be challenging in terms of proper model specification. This complex task can be simplified using regularization techniques such as gradient boosting algorithms, but the estimates derived from such models are shrunken towards zero and it is consequently not straightforward to calculate proper confidence intervals or test statistics. In this article, we propose two strategies to obtain p-values for linear effect estimates for Gaussian location and scale models based on permutation tests and a parametric bootstrap approach. These procedures can provide a solution for one of the remaining problems in the application of gradient boosting algorithms for distributional regression in biostatistical data analyses. Results from extensive simulations indicate that in low-dimensional data both suggested approaches are able to hold the type-I error threshold and provide reasonable test power comparable to the Wald-type test for maximum likelihood inference. In high-dimensional data, when gradient boosting is the only feasible inference for this model class, the power decreases but the type-I error is still under control. In addition, we demonstrate the application of both tests in an epidemiological study to analyse the impact of physical exercise on both average and the stability of the lung function of elderly people in Germany.

Keywords: boosting; GAMLSS; regularized regression; permutation test; parametric bootstrap (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/ijb-2018-0110

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