Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Thomas Kneib & Torsten Hothorn & Gerhard Tutz, 2009. "Variable Selection and Model Choice in Geoadditive Regression Models," Biometrics, The International Biometric Society, vol. 65(2), pages 626-634, June.
- Fengting Yi & Niansheng Tang & Jianguo Sun, 2022. "Simultaneous variable selection and estimation for joint models of longitudinal and failure time data with interval censoring," Biometrics, The International Biometric Society, vol. 78(1), pages 151-164, March.
- Boyao Zhang & Tobias Hepp & Sonja Greven & Elisabeth Bergherr, 2022. "Adaptive step-length selection in gradient boosting for Gaussian location and scale models," Computational Statistics, Springer, vol. 37(5), pages 2295-2332, November.
- Andreas Mayr & Nora Fenske & Benjamin Hofner & Thomas Kneib & Matthias Schmid, 2012. "Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(3), pages 403-427, May.
- Gerhard Tutz & Harald Binder, 2006. "Generalized Additive Modeling with Implicit Variable Selection by Likelihood-Based Boosting," Biometrics, The International Biometric Society, vol. 62(4), pages 961-971, December.
- Bissantz, Nicolai & Hohage, T. & Munk, Axel & Ruymgaart, F., 2007. "Convergence rates of general regularization methods for statistical inverse problems and applications," Technical Reports 2007,04, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- Rizopoulos, Dimitris, 2010. "JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i09).
- Zangdong He & Wanzhu Tu & Sijian Wang & Haoda Fu & Zhangsheng Yu, 2015. "Simultaneous variable selection for joint models of longitudinal and survival outcomes," Biometrics, The International Biometric Society, vol. 71(1), pages 178-187, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Philip Kostov, 2010. "Do Buyers’ Characteristics and Personal Relationships Affect Agricultural Land Prices?," Land Economics, University of Wisconsin Press, vol. 86(1), pages 48-65.
- Hendrik van der Wurp & Andreas Groll, 2023. "Introducing LASSO-type penalisation to generalised joint regression modelling for count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 127-151, March.
- Fabian Scheipl & Thomas Kneib & Ludwig Fahrmeir, 2013. "Penalized likelihood and Bayesian function selection in regression models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 349-385, October.
- Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
- Colin Griesbach & Andreas Groll & Elisabeth Bergherr, 2021. "Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
- Benjamin Hofner & Torsten Hothorn & Thomas Kneib, 2013. "Variable selection and model choice in structured survival models," Computational Statistics, Springer, vol. 28(3), pages 1079-1101, June.
- Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
- Hongyuan Cao & Mathew M. Churpek & Donglin Zeng & Jason P. Fine, 2015. "Analysis of the Proportional Hazards Model With Sparse Longitudinal Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1187-1196, September.
- Benjamin Hofner & Andreas Mayr & Nikolay Robinzonov & Matthias Schmid, 2014. "Model-based boosting in R: a hands-on tutorial using the R package mboost," Computational Statistics, Springer, vol. 29(1), pages 3-35, February.
- Xiaohong Chen & Demian Pouzo, 2012.
"Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals,"
Econometrica, Econometric Society, vol. 80(1), pages 277-321, January.
- Xiaohong Chen & Demian Pouzo, 2008. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Cowles Foundation Discussion Papers 1650R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2009.
- Xiaohong Chen & Demian Pouzo, 2008. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Cowles Foundation Discussion Papers 1650RR, Cowles Foundation for Research in Economics, Yale University, revised Jan 2011.
- Riccardo De Bin & Vegard Grødem Stikbakke, 2023. "A boosting first-hitting-time model for survival analysis in high-dimensional settings," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 420-440, April.
- Johannes, Jan & Van Bellegem, Sébastien & Vanhems, Anne, 2011.
"Convergence Rates For Ill-Posed Inverse Problems With An Unknown Operator,"
Econometric Theory, Cambridge University Press, vol. 27(3), pages 522-545, June.
- Johannes, Jan & Van Bellegem, Sébastien & Vanhems, Anne, 2009. "Convergence Rates for III-Posed Inverse Problems with an Unknown Operator," TSE Working Papers 09-030, Toulouse School of Economics (TSE).
- JOHANNES, Jan & VAN BELLEGEM, Sébastien & VANHEMS, Anne, 2011. "Convergence rates for ill-posed inverse problems with an unknown operator," LIDAM Reprints CORE 2330, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
- Andrews, Donald W.K., 2017.
"Examples of L2-complete and boundedly-complete distributions,"
Journal of Econometrics, Elsevier, vol. 199(2), pages 213-220.
- Donald W.K. Andrews, 2011. "Examples of L^2-Complete and Boundedly-Complete Distributions," Cowles Foundation Discussion Papers 1801, Cowles Foundation for Research in Economics, Yale University.
- Oi, Katsuya, 2020. "Disuse as time away from a cognitively demanding job; how does it temporally or developmentally impact late-life cognition?," Intelligence, Elsevier, vol. 82(C).
- Xin Fang & Bo Fang & Chunfang Wang & Tian Xia & Matteo Bottai & Fang Fang & Yang Cao, 2019. "Comparison of Frequentist and Bayesian Generalized Additive Models for Assessing the Association between Daily Exposure to Fine Particles and Respiratory Mortality: A Simulation Study," IJERPH, MDPI, vol. 16(5), pages 1-20, March.
- Elisabeth Waldmann & Thomas Kneib & Yu Ryan Yu & Stefan Lang, 2012. "Bayesian semiparametric additive quantile regression," Working Papers 2012-06, Faculty of Economics and Statistics, Universität Innsbruck.
- Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
- Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
- Hofner, Benjamin & Mayr, Andreas & Schmid, Matthias, 2016. "gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i01).
More about this item
Keywords
joint modeling; time-to-event analysis; gradient boosting; statistical learning; variable selection;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:411-:d:1034015. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.