{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:15:58Z","timestamp":1770833758979,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T00:00:00Z","timestamp":1478217600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T00:00:00Z","timestamp":1478217600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01CA16373"],"award-info":[{"award-number":["R01CA16373"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["RO1HL103552"],"award-info":[{"award-number":["RO1HL103552"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2017,2]]},"DOI":"10.1007\/s10994-016-5597-1","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T18:41:00Z","timestamp":1478284860000},"page":"277-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Boosted multivariate trees for longitudinal data"],"prefix":"10.1007","volume":"106","author":[{"given":"Amol","family":"Pande","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeevanantham","family":"Rajeswaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Ehrlinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Udaya B.","family":"Kogalur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugene H.","family":"Blackstone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hemant","family":"Ishwaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,11,4]]},"reference":[{"key":"5597_CR1","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. California: Belmont."},{"key":"5597_CR2","doi-asserted-by":"crossref","unstructured":"De Boor, C. (1978). A practical guide to splines. Berlin: Springer.","DOI":"10.1007\/978-1-4612-6333-3"},{"key":"5597_CR3","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198524847.001.0001","volume-title":"Analysis of longitudinal data","author":"P Diggle","year":"2002","unstructured":"Diggle, P., Heagerty, P., Liang, K.-Y., & Zeger, S. (2002). Analysis of longitudinal data. Oxford: Oxford University Press."},{"key":"5597_CR4","unstructured":"Duchon, J. (1977). Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In Constructive theory of functions of several variables (pp. 85\u2013100). Berlin Heidelberg: Springer."},{"key":"5597_CR5","doi-asserted-by":"crossref","unstructured":"Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89\u2013102.","DOI":"10.1214\/ss\/1038425655"},{"key":"5597_CR6","unstructured":"Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the 13th international conference on machine learning (pp. 148\u2013156)."},{"key":"5597_CR7","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189\u20131232.","journal-title":"Annals of Statistics"},{"issue":"4","key":"5597_CR8","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","volume":"38","author":"JH Friedman","year":"2002","unstructured":"Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367\u2013378.","journal-title":"Computational Statistics & Data Analysis"},{"issue":"2","key":"5597_CR9","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3414\/ME11-02-0021","volume":"51","author":"A Groll","year":"2012","unstructured":"Groll, A., & Tutz, G. (2012). Regularization for generalized additive mixed models by likelihood-based boosting. Methods of Information in Medicine, 51(2), 168.","journal-title":"Methods of Information in Medicine"},{"key":"5597_CR10","volume-title":"Generalized additive models","author":"TJ Hastie","year":"1990","unstructured":"Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models (Vol. 43). Boca raton: CRC Press."},{"issue":"4","key":"5597_CR11","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1093\/biomet\/85.4.809","volume":"85","author":"DR Hoover","year":"1998","unstructured":"Hoover, D. R., Rice, J. A., Wu, C. O., & Yang, L.-P. (1998). Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data. Biometrika, 85(4), 809\u2013822.","journal-title":"Biometrika"},{"key":"5597_CR12","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1198\/106186006X133933","volume":"15","author":"T Hothorn","year":"2006","unstructured":"Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical statistics, 15, 651\u2013674.","journal-title":"Journal of Computational and Graphical statistics"},{"key":"5597_CR13","first-page":"2109","volume":"11","author":"T Hothorn","year":"2010","unstructured":"Hothorn, T., Buhlmann, P., Kneib, T., Schmid, M., & Hofner, B. (2010). Model-based boosting 2.0. Journal of Machine Learning Research, 11, 2109\u20132113.","journal-title":"Journal of Machine Learning Research"},{"key":"5597_CR14","unstructured":"Hothorn, T., Buhlmann, P., Kneib, T., Schmid, M., Hofner, B., Sobotka, A., & Scheipl, F. (2016). mboost: Model-based boosting, 2016. R package version 2.6-0."},{"key":"5597_CR15","unstructured":"Ishwaran, H., & Kogalur, U. B. (2016). Random forests forsurvival, regression and classification (RF-SRC), 2016. R packageversion 2.2.0."},{"key":"5597_CR16","unstructured":"Ishwaran, H., Pande, A., & Kogalur, U. B. (2016). Boostmtree: Boosted multivariate trees for longitudinaldata, 2016. R package version 1.1.0."},{"key":"5597_CR17","first-page":"815","volume":"7","author":"W-Y Loh","year":"1997","unstructured":"Loh, W.-Y., & Shih, Y.-S. (1997). Split selection methods for classification trees. Statistica Sinica, 7, 815\u2013840.","journal-title":"Statistica Sinica"},{"key":"5597_CR18","doi-asserted-by":"publisher","first-page":"3397","DOI":"10.1109\/78.258082","volume":"41","author":"S Mallat","year":"1993","unstructured":"Mallat, S., & Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41, 3397\u20133415.","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"1","key":"5597_CR19","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.jtcvs.2012.03.028","volume":"144","author":"DP Mason","year":"2012","unstructured":"Mason, D. P., Rajeswaran, J., Liang, L., Murthy, S. C., Su, J. W., Pettersson, G. B., et al. (2012). Effect of changes in postoperative spirometry on survival after lung transplantation. The Journal of Thoracic and Cardiovascular Surgery, 144(1), 197\u2013203.","journal-title":"The Journal of Thoracic and Cardiovascular Surgery"},{"issue":"1","key":"5597_CR20","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/1471-2288-12-6","volume":"12","author":"A Mayr","year":"2012","unstructured":"Mayr, A., Hothorn, T., & Fenske, N. (2012). Prediction intervals for future BMI values of individual children-a non-parametric approach by quantile boosting. BMC Medical Research Methodology, 12(1), 6.","journal-title":"BMC Medical Research Methodology"},{"key":"5597_CR21","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3414\/ME11-02-0030","volume":"51","author":"A Mayr","year":"2012","unstructured":"Mayr, A., Hofner, B., & Schmid, M. (2012). The importance of knowing when to stop: A sequential stopping rule for component-wise gradient boosting. Methods of Information in Medicine, 51, 178\u2013186.","journal-title":"Methods of Information in Medicine"},{"key":"5597_CR22","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1111\/j.0006-341X.2001.00120.x","volume":"57","author":"W Pan","year":"2001","unstructured":"Pan, W. (2001). Akaike\u2019s information criteria in generalized estimating equations. Biometrika, 57, 120\u2013125.","journal-title":"Biometrika"},{"key":"5597_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-0318-1","volume-title":"Mixed-effects models in S and S-PLUS","author":"JC Pinheiro","year":"2000","unstructured":"Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. Berlin: Springer."},{"key":"5597_CR24","unstructured":"Pinheiro, J.C., Bates, D.M., DebRoy, S., Sarkar, D., & R Core Team. (2014).nlme: Linear and nonlinear mixed effects models. Rpackage version 3.1-117."},{"key":"5597_CR25","doi-asserted-by":"crossref","unstructured":"Robinson, G. K. (1991). That BLUP is a good thing: The estimation of random effects. Statistical Science, 6(1), 15\u201332.","DOI":"10.1214\/ss\/1177011926"},{"key":"5597_CR26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511755453","volume-title":"Semiparametric regression.","author":"D Ruppert","year":"2003","unstructured":"Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric regression. (Vol. 12). Cambridge: Cambridge University Press."},{"key":"5597_CR27","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s10994-011-5258-3","volume":"86","author":"RJ Sela","year":"2012","unstructured":"Sela, R. J., & Simonoff, J. S. (2012). RE-EM trees: A data mining approach for longitudinal and clustered data. Machine Learning, 86, 169\u2013207.","journal-title":"Machine Learning"},{"issue":"4","key":"5597_CR28","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1111\/j.1541-0420.2006.00578.x","volume":"62","author":"G Tutz","year":"2006","unstructured":"Tutz, G., & Binder, H. (2006). Generalized additive modeling with implicit variable selection by likelihood-based boosting. Biometrics, 62(4), 961\u2013971.","journal-title":"Biometrics"},{"issue":"14","key":"5597_CR29","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1002\/sim.2738","volume":"26","author":"G Tutz","year":"2007","unstructured":"Tutz, G., & Reithinger, F. (2007). A boosting approach to flexible semiparametric mixed models. Statistics in Medicine, 26(14), 2872\u20132900.","journal-title":"Statistics in Medicine"},{"key":"5597_CR30","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970128","volume-title":"Spline models for observational data","author":"G Wahba","year":"1990","unstructured":"Wahba, G. (1990). Spline models for observational data (Vol. 59). Bangkok: SIAM."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-016-5597-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-016-5597-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-016-5597-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T10:53:34Z","timestamp":1718880814000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-016-5597-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,4]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,2]]}},"alternative-id":["5597"],"URL":"https:\/\/doi.org\/10.1007\/s10994-016-5597-1","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11,4]]},"assertion":[{"value":"12 April 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2016","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2016","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}