Addressing cluster-constant covariates inmixed effects models via likelihood-based boosting techniques

• Colin Griesbach ^[1] ; Andreas Groll ; Elisabeth Waldmann ^[2]
  1. [1] Friedrich-Alexander University Erlangen-Nürnberg
  2. [2] TU Dortmund University
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 108-113
• Idioma: inglés
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• Resumen

  • : Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated in two general approaches, namely gradient boosting and likelihood-based boosting. An extensive framework has been proposed in order to fit generalised mixed models based on boosting, however for the case of cluster-constant covariates likelihood-based boosting approaches tend to mischoose variables in the selection step leading to wrong estimates. We propose an improved boosting algorithm for linear mixed models, where the random effects are properly weighted, disentangled from the fixed effects updating scheme and corrected for correlations with cluster-constant covariates in order to improve quality of estimates and in addition reduce the computational effort.

    The method outperforms current state-of-the-art approaches from boosting and maximum likelihood inference.