Gradient Tree Boosting for Hierarchical Data

References

• Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
   Web of Science ®Google Scholar
• Boehmke, B., & Greenwell, B. (2020). Hands-on machine learning with R. Chapman and Hall/CRC.
   Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth.
   Google Scholar
• Bühlmann, P., & Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting (with discussion). Statistical Science, 22(4), 477–505.
   Web of Science ®Google Scholar
• Capitaine, L. (2020). Longiturf: Random forests for longitudinal data [Computer software manual]. https://CRAN.R-project.org/package=LongituRF (R package version 0.9)
   Google Scholar
• Capitaine, L., Genuer, R., & Thiébaut, R. (2021). Random forests for high-dimensional longitudinal data. Statistical Methods in Medical Research, 30(1), 166–184.
   PubMed Web of Science ®Google Scholar
• Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). Bart: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266–298. https://doi.org/10.1214/09-AOAS285
   Web of Science ®Google Scholar
• De’Ath, G. (2002). Multivariate regression trees: a new technique for modeling species–environment relationships. Ecology, 83(4), 1105–1117.
   Web of Science ®Google Scholar
• Dorie, V. (2022). stan4bart: Bayesian additive regression trees with stan-sampled parametric extensions [Computer software manual]. https://github.com/vdorie/stan4bart (R package version 0.0-4)
   Google Scholar
• Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14, 91–118.
   PubMed Web of Science ®Google Scholar
• Fahrmeir, L., Kneib, T., Lang, S., & Marx, B. (2013). Regression: Models, methods and applications. Springer.
   Google Scholar
• Fokkema, M., Smits, N., Zeileis, A., Hothorn, T., & Kelderman, H. (2018). Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees. Behavior Research Methods, 50(5), 2016–2034. https://doi.org/10.3758/s13428-017-0971-x
   PubMed Web of Science ®Google Scholar
• Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
   Web of Science ®Google Scholar
• Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
   Web of Science ®Google Scholar
• Fu, W., & Simonoff, J. S. (2015). Unbiased regression trees for longitudinal and clustered data. Computational Statistics & Data Analysis, 88, 53–74. https://doi.org/10.1016/j.csda.2015.02.004
   Web of Science ®Google Scholar
• Galimberti, G., & Montanari, A. (2002). Regression trees for longitudinal data with time-dependent covariates. In Classification, clustering, and data analysis (pp. 391–398). Springer.
   Google Scholar
• Groll, A., & Tutz, G. (2014). Variable selection for generalized linear mixed models by l1-penalized estimation. Statistics and Computing, 24(2), 137–154. https://doi.org/10.1007/s11222-012-9359-z
   Web of Science ®Google Scholar
• Haines-Delmont, A., Chahal, G., Bruen, A. J., Wall, A., Khan, C. T., Sadashiv, R., & Fearnley, D. (2020). Testing suicide risk prediction algorithms using phone measurements with patients in acute mental health settings: Feasibility study. JMIR mHealth and uHealth, 8(6), e15901. https://doi.org/10.2196/15901
   PubMed Web of Science ®Google Scholar
• Hajjem, A., Bellavance, F., & Larocque, D. (2011). Mixed effects regression trees for clustered data. Statistics & Probability Letters, 81(4), 451–459. https://doi.org/10.1016/j.spl.2010.12.003
   Web of Science ®Google Scholar
• Hajjem, A., Bellavance, F., & Larocque, D. (2014). Mixed-effects random forest for clustered data. Journal of Statistical Computation and Simulation, 84(6), 1313–1328. https://doi.org/10.1080/00949655.2012.741599
   Web of Science ®Google Scholar
• Harlow, L. L., & Oswald, F. L. (2016). Big data in psychology: Introduction to the special issue. Psychological Methods, 21(4), 447–457. https://doi.org/10.1037/met0000120
   PubMed Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
   Google Scholar
• Henninger, M., Debelak, R., Rothacher, Y., & Strobl, C. (2022). Interpretable machine learning for psychological research: Opportunities and pitfalls.
   Google Scholar
• Hofner, B., Hothorn, T., Kneib, T., & Schmid, M. (2011). A framework for unbiased model selection based on boosting. Journal of Computational and Graphical Statistics, 20(4), 956–971. https://doi.org/10.1198/jcgs.2011.09220
   Web of Science ®Google Scholar
• Hofner, B., Mayr, A., Robinzonov, N., & Schmid, M. (2014). Model-based boosting in r: a hands-on tutorial using the r package mboost. Computational Statistics, 29(1-2), 3–35. https://doi.org/10.1007/s00180-012-0382-5
   Web of Science ®Google Scholar
• Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M., Hofner, B. (2022). mboost: Model-based boosting [Computer software manual]. https://CRAN.R-project.org/package=mboost (R package version 2.9-7)
   Google Scholar
• Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3), 651–674. https://doi.org/10.1198/106186006X133933
   Web of Science ®Google Scholar
• Jennrich, R. I., & Schluchter, M. D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 42(4), 805–820. https://doi.org/10.2307/2530695
   PubMed Web of Science ®Google Scholar
• Jiang, J. (2007). Linear and generalized linear mixed models and their applications. Springer.
   Google Scholar
• Ju, X., & Salibián-Barrera, M. (2021). Robust boosting for regression problems. Computational Statistics & Data Analysis, 153, 107065. https://doi.org/10.1016/j.csda.2020.107065
   Web of Science ®Google Scholar
• Kundu, M. G., & Harezlak, J. (2019). Regression trees for longitudinal data with baseline covariates. Biostatistics & Epidemiology, 3(1), 1–22. https://doi.org/10.1080/24709360.2018.1557797
   PubMedGoogle Scholar
• Liaw, A., & Wiener, M. (2002). Classification and regression by randomforest. R News, 2(3), 18–22. https://CRAN.R-project.org/doc/Rnews/
   Google Scholar
• Loh, W.-Y. (2002). Regression tress with unbiased variable selection and interaction detection. Statistica Sinica, 12, 361–386.
   Web of Science ®Google Scholar
• Loh, W.-Y., & Zheng, W. (2013). Regression trees for longitudinal and multiresponse data. The Annals of Applied Statistics, 7(1), 495–522.
   Web of Science ®Google Scholar
• Lutz, R. W., Kalisch, M., & Bühlmann, P. (2008). Robustified l2 boosting. Computational Statistics & Data Analysis, 52(7), 3331–3341. https://doi.org/10.1016/j.csda.2007.11.006
   Web of Science ®Google Scholar
• Nestler, S., & Humberg, S. (2022). A lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation. Psychometrika, 87(2), 506–532. https://doi.org/10.1007/s11336-021-09787-w
   PubMed Web of Science ®Google Scholar
• Ngufor, C., Van Houten, H., Caffo, B. S., Shah, N. D., & McCoy, R. G. (2019). Mixed effect machine learning: a framework for predicting longitudinal change in hemoglobin a1c. Journal of Biomedical Informatics, 89, 56–67.
   PubMed Web of Science ®Google Scholar
• Pan, J., & Huang, C. (2014). Random effects selection in generalized linear mixed models via shrinkage penalty function. Statistics and Computing, 24(5), 725–738. https://doi.org/10.1007/s11222-013-9398-0
   Web of Science ®Google Scholar
• Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., Core Team, R. (2021). nlme: Linear and nonlinear mixed effects models [Computer software manual]. https://CRAN.R-project.org/package=nlme (R package version 3.1-153)
   Google Scholar
• Schelldorfer, J., Bühlmann, P., & van de Geer, S. (2011). Estimation for high-dimensional linear mixed-effects models using L1-penalization. Scandinavian Journal of Statistics, 38(2), 197–214. https://doi.org/10.1111/j.1467-9469.2011.00740.x
   Web of Science ®Google Scholar
• Schwartz, B., Cohen, Z. D., Rubel, J. A., Zimmermann, D., Wittmann, W. W., & Lutz, W. (2021). Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. Psychotherapy Research: Journal of the Society for Psychotherapy Research, 31(1), 33–51. https://doi.org/10.1080/10503307.2020.1769219
   PubMed Web of Science ®Google Scholar
• Segal, M. R. (1992). Tree-structured methods for longitudinal data. Journal of the American Statistical Association, 87(418), 407–418. https://doi.org/10.1080/01621459.1992.10475220
   Web of Science ®Google Scholar
• Sela, R. J., & Simonoff, J. S. (2012). RE-EM trees: A data mining approach for longitudinal and clustered data. Machine Learning, 86(2), 169–207. https://doi.org/10.1007/s10994-011-5258-3
   Web of Science ®Google Scholar
• Sela, R. J., Simonoff, J. S. (2011). Reemtree: Regression trees with random effects [Computer software manual]. https://CRAN.R-project.org/package=REEMtree (R package version 0.90.3)
   Google Scholar
• Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford university press.
   Google Scholar
• Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis. SAGE Publications.
   Google Scholar
• Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(1), 1–21. https://doi.org/10.1186/1471-2105-8-25
   PubMed Web of Science ®Google Scholar
• Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0016973
   PubMed Web of Science ®Google Scholar
• Tan, Y. V., Flannagan, C. A., & Elliott, M. R. (2018). Predicting human-driving behavior to help driverless vehicles drive: random intercept bayesian additive regression trees. Statistics and Its Interface, 11(4), 557–572. https://doi.org/10.4310/SII.2018.v11.n4.a1
   Web of Science ®Google Scholar
• Therneau, T., Atkinson, B. (2019). rpart: Recursive partitioning and regression trees [Computer software manual]. https://CRAN.R-project.org/package=rpart (R package version 4.1-15)
   Google Scholar
• Verbeke, G., & Molenberghs, G. (2009). Linear mixed models for longitudinal data. Springer.
   Google Scholar
• Wilcox, R. (2012). Introduction to robust estimation and hypothesis testing (3rd ed.). Academic Press.
   Google Scholar
• Wu, H., & Zhang, J. T. (2006). Nonparametric regression methods for longitudinal data analysis. Wiley.
   Google Scholar
• Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 12(6), 1100–1122.
   PubMedGoogle Scholar
• Zeileis, A., Hothorn, T., & Hornik, K. (2008). Model-based recursive partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514. https://doi.org/10.1198/106186008X319331
   Web of Science ®Google Scholar