Brandmaier, A. M. (2024). Machine learning for mobile sensing data. In M. R. Mehl, M. Eid, C. Wrzus, G. M. Harari, & U. W. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications (pp. 409–431). Guilford Press.
Brandmaier, A. M. (2024). Big data dimensionality reduction methods. In M. R. Mehl, M. Eid, C. Wrzus, G. M. Harari, & U. W. Ebner-Priemer (Eds.), Mobile sensing in psychology: Methods and applications (pp. 456–476). Guilford Press.
Brandmaier, A. M., & Jacobucci, R. C. (2023). Machine learning approaches to structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed., pp. 722–739). Guilford Press.
Brandmaier, A. M., Driver, C. C., & Voelkle, M. C. (2018). Recursive partitioning in continuous time analysis. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 259–282). Springer.
Brandmaier, A. M., von Oertzen, T., McArdle, J. J., & Lindenberger, U. (2014). Exploratory data mining with structural equation model trees. In J. J. McArdle & G. Ritschard (Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (pp. 96–127). Routledge.
Peikert, A. (2023). Towards transparency and Open Science: A principled perspective on computational reproducibility and preregistration [PhD Thesis, Humboldt-Universität zu Berlin]. https://doi.org/10.18452/27056
Brandmaier, A. M. (2011). Permutation distribution clustering and structural equation model trees [PhD Thesis, Universität des Saarlandes Saarbrücken].
Brandmaier, A. M., Ram, N., Wagner, G. G., & Gerstorf, D. (2017). Terminal decline in well-being: The role of multi-indicator constellations of physical health and psychosocial correlates (SOEPpapers on Multidisciplinary Panel Data Research No. 912). DIW.